Deepfakes: evolution and trends

Abstract

This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

Full text

Soft Computing (2023) 27:11295–11318
https://doi.org/10.1007/s00500-023-08605-y
DATA ANALYTICS AND MACHINE LEARNING
Deepfakes: evolution and trends
Rosa Gil1·Jordi Virgili-Gomà1·Juan-Miguel López-Gil2·Roberto García1
Accepted: 21 May 2023 / Published online: 15 June 2023
© The Author(s) 2023
Abstract
This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles
published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What
are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How
do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We
have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus
databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which
deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that
currently have the most potential for development; most studied topics on deepfakes research, including the different artificial
intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries
where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities
in deepfakes research for practitioners and researchers who want to get into this topic.
Keywords Deepfakes ·Artificial intelligence ·Deep learning ·Bibliometrics
1 Introduction
Deepfake technology can be used to forge synthetic media
that people cannot differentiate from true ones. It is a recent
research area in which researchers in academia and indus-
try have contributed deepfake databases, and synthesis and
detection algorithms, which has made the deepfake popular-
ity grow. Deepfakes are the product of artificial intelligence
(AI) applications that merge, combine, replace, and super-
impose images and video clips to create fake videos that
appear authentic (Maras and Alexandrou 2019). Deepfakes
use recent advances in deep neural networks to create hyper-
realistic synthetic media. When deepfake technology is used
BRoberto García
roberto.garcia@udl.cat
Rosa Gil
rosamaria.gil@udl.cat
Jordi Virgili-Gomà
jordi.virgili@udl.cat
Juan-Miguel López-Gil
juanmiguel.lopez@ehu.eus
1Universitat de Lleida, 25001 Lleida, Spain
2University of the Basque Country, 20018 Donostia-San
Sebastián, Spain
on videos or images, the face of a person can be swapped
with another face leaving little trace of manipulation (Chawla
2019). The emergence of deep learning has made previously
existing fake face detection strategies vulnerable (Cho and
Jeong 2017).
The availability of deepfake databases and synthesis and
detection algorithms have made it possible for the commu-
nity and even amateurish users to perform realistic deepfakes,
which in turn has made the amount of popularity deepfake
videos in the wild grow immensely (Pu et al. 2021a). Coupled
with the reach and speed of social media, convincing deep-
fakes can quickly reach millions of people and have negative
impacts on our society (Westerlund 2019).
The growth in deepfakes research has also been reflected
in the amount of related scientific literature. Apart from tech-
nological aspects related to deepfake creation and detection,
ethical, social, and legal aspects have also been carefully
analyzed. There are already some reviews in specific fields,
such as Creation and detection of deepfakes (Mirsky and
Lee 2021), Law (da Silva 2021), Forensics (Amerini et al.
2021a), and Social impact (Hancock and Bailenson 2021a),
to name a few. Still, none of them contemplates the full spec-
trum of research areas in deepfakes, which we believe can
be very useful for researchers who wish to work on this
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11296 R. Gil et al.
research topic. Despite its novelty, deepfakes research is a
fast-growing research area, in which the research topics and
their relationship is continuously changing over time and
new trends appear. The different areas in which deepfakes
research is performed indicate there are researchers with a
wide variety of backgrounds. Apart from current trends, ana-
lyzing the funding opportunities is interesting to help focus
the research effort.
The objective of this work is to get an overview of the cur-
rent trends and evolution of deepfakes research, as well as to
analyze the fields in which it is being applied. To this aim, all
the empirical evidence that fits pre-specified eligibility cri-
teria to answer the following six specific research questions
was collated in Scopus and Web of Science databases: What
are the main research areas of the articles in deepfakes? What
are the main current topics in deepfakes research and how are
they related? Which are the trends in deepfakes research?
How do topics in deepfakes research change over time?
Who is researching deepfakes? Who is funding deepfakes
research? It has been decided which disciplines are develop-
ing, which are consolidating, and which are promising. The
most studied areas of deep learning research, including the
various artificial intelligence techniques used, have also been
examined, along with emerging and niche topics. Relation-
ships between the most well-known scientists, the nations
where deepfakes research is conducted, and the major fund-
ing organizations have also been established. The prospects
and trends in deepfakes research are identified in this arti-
cle for practitioners and scholars who are interested in the
subject.
The remainder of this paper is structured as follows. The
next section presents the methods used to obtain the sample of
articles to study that determine the focus, the specific research
questions we seek to answer, and the software used to auto-
mate part of the process. In the results section, we expose
the findings of specified research questions. After providing
some reflections on the discussion, conclusions are drawn.
2 Methods
A systematic review attempts to collate all the empirical evi-
dence that fits pre-specified eligibility criteria to answer a
specific research question (Higgins et al. 2019). Therefore,
the authors have ensured that the review addresses relevant
questions to those who are expected to use and act upon
its conclusions. More specifically, the research questions
addressed by this review paper are:
•RQ1: What are the main research areas of the articles in
deepfakes?
•RQ2: What are the main current topics in deepfakes
research and how are they related?
Table 1 Records retrieved from Scopus and Web of Science in July and
October 2021, between parentheses those in English
Results (in English)
July 2021 October 2021 Growth
Scopus 242 (229) 331 (311) 89 (82)
Web of Science 8 (6) 12 (10) 4 (4)
•RQ3: Which are the trends in deepfakes research?
•RQ4: How do topics in deepfakes research change over
time?
•RQ5: Who is researching deepfakes?
•RQ6: Who is funding deepfakes research?
Once the research questions were established, the starting
point was a search carried out in Scopus in July 2021 and
another in October of the same year. The specific query used
in the case of Scopus was:
ALL (
( deepfake deep-fake "deep fake" ) AND
( ( action unit OR facial action unit
coding system OR facs ) OR ( video
OR clip OR image OR photogram )
))
The same procedure was followed in Web of Science
(WoS) also in July and October. The query in the case of
WoS wa s :
TS=(
( deepfake deep-fake "deep fake" )
AND ((Action Unit OR Facial Action
Unit Coding System OR FACS) OR
(video OR clip OR image OR photogram)))
As summarized in Table 1, the Scopus query retrieved a
total of 242 records (229 in English) in July and 331 (311
in English) in October. The range of years for the retrieved
records was from 2018 to 2021. There were no results before
2018 from any of the databases. In the case of Web of Science,
the results were 8 in July (6 in English) and 12 in October
(10inEnglish).
The first objective of these queries was to check if the same
articles were being published in both databases and to esti-
mate the rate of growth of the number of publications from
the change between the July and October requests. Given
the small number of results from Web of Science, and that
just one of them is not present in the Scopus results, the
detailed analysis focuses on the October results in English,
i.e., 311 records from Scopus, from now on the SDO21 (Sco-
pus Database October 2021) dataset. The dataset records are
listed in Appendix A, divided into clusters based on their
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Deepfakes: evolution... 11297
keywords, and available for download online.1It is also
important to note the importance that conference publications
have concerning deepfakes research as they are not included
in the Web of Science. One hundred and seventy-nine of the
records from Scopus are conference papers.
Given the size of the SDO21 dataset, the review has
been automated using the Bibliometrix (Aria and Cuccurullo
2017) package for R, including the Biblioshiny application,
as detailed in Sect. 3. Regarding transparent reporting of sys-
tematic review and meta-analysis, a PRISMA Flow Diagram
2has not been considered necessary because the process has
been simple. All the records retrieved have been considered,
with the only exception of articles not in English, to facili-
tate the automated analysis using Bibliometrix. In any case,
as observed in Table 1, the number of records that are not in
English just represents between 5% and 6% of the results, in
July and October, respectively.
3 Results
3.1 Main topics in deepfakes research
Regarding the first two research questions,RQ1: What are the
main research areas of the articles in deepfakes? and RQ2:
What are the main current topics in deepfakes research and
how are they related?, our first exploration considers just the
review papers, the focus of which is mainly placed on ethical
and legal aspects as detailed next:
•Forensics (Amerini et al. 2021a; Castillo Camacho and
Wang 2021a)
•Pornography (Karasavva and Noorbhai 2021a)
•Law (O’Donnell 2021; da Silva 2021; Aboueldahab and
Freixo 2021a; Colon 2020a;Meskysetal.2020a;Farish
2020a; Perot and Mostert 2020a)
•Theater (Fletcher 2018a)
•Social impact (Hancock and Bailenson 2021a)
•Social spam (Rao et al. 2021)
•Creation and detection of deepfakes (Mirsky and Lee
2021).
If we broaden to the whole set of 311 papers and just
analyze the research areas they belong to, Computer Science
is the most represented with 40.8% of the records related to
this area. It is followed by Engineering (19,5%) and Social
Sciences (9,4%), as shown in Fig. 1. It is important to note that
papers might belong to more than one area, as defined by the
corresponding literature database for each journal and year.
1Replication data, https://doi.org/10.34810/data750.
2http://prisma-statement.org.
We consider all areas when calculating these percentages as
a way to recognize the interdisciplinary nature of deepfakes,
with scientific journals aiming to promote interdisciplinary
research and facilitate collaboration among researchers with
diverse expertise.
To get deeper into the specific topics deepfakes research
is dealing with, a knowledge discovery approach has been
applied to identify the underlying conceptual structure. The
keywords associated with each record in the SDO21 dataset
have been analyzed with the Bibliometrix R package. The
conceptual structure represents the relationship among the
records’ keywords. Keywords that appear together in a paper
corresponding to a record are connected in the resulting co-
keywords network. Keywords will be close in this network if
a large proportion of papers have them together. Otherwise,
they will be apart.
The process to create this co-keywords network that
highlights the main research topics is first to create a
co-occurrence symmetric matrix. As shown in Fig.2, the ele-
ments in the diagonal kii correspond to the total amount of
occurrences of each keyword in the whole SDO21 dataset.
On the other hand, the element outside of the diagonal, kij,
corresponds to how many times the keyword iand keyword
jappear together in the same paper.
The co-keywords matrix is then used to generate the key-
words network that highlights the research topics structure
in deepfakes research. The network is an undirected graph
where the nodes correspond to keywords and whose size
depends on the keyword frequency, thus generated from the
matrix’s diagonal.
Then, two graph nodes are connected if the matrix cell
for the corresponding keywords is greater than 0, and thus
both keywords share at least one paper. The edges are
weighted with the value of that cell, i.e., the number of papers
where both keywords appear together as captured in the non-
diagonal cells. Edges’ weight is interpreted as a measure of
the strength between two keywords, the higher they appear
the closer they are on the graph. Based on this interpretation
of the matrix, the graph can be rendered as shown in Fig.3
and highlights the main research topics corresponding to the
most frequent keywords. This technique processes keywords
as text strings and thus does not include any kind of seman-
tic similarity measure. It focuses on the keywords associated
with each publication.
Co-occurrence networks use various measures to iden-
tify crucial nodes or vertices within the network. Among
these measures, Betweenness (Table 2), Closeness (Table 3),
and PageRank (Table 4) are used to provide notable insights.
When considering the top 5 keywords for each metric, a sum
of 8 unique keywords is obtained. This is consistent as each
measure is capturing a different aspect of the network of
keyword co-occurrences. Betweenness quantifies how often
a node falls on the shortest paths between other nodes in
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11298 R. Gil et al.
Fig. 1 Main research areas for
the papers included in the
SDO21 dataset (311 records
retrieved from Scopus on
October 2021)
Fig. 2 Co-keyword matrix used to generate the network highlighting
the research topics in deepfakes research shown in Fig.3
the network. Nodes with high Betweenness are critical since
they connect different parts of the network, playing a vital
role in the flow of information or resources between distinct
groups of nodes. Closeness measures how closely connected
a node is to all other nodes in the network. Nodes with high
Closeness are significant since they have rapid access to a
vast amount of information or resources and can disseminate
them quickly throughout the network. PageRank assesses a
node’s importance based on the number and quality of incom-
ing links it has. Nodes with high PageRank are crucial since
they are highly connected to other important nodes in the
network. In identifying key intermediaries or brokers in the
network, Betweenness is the most critical measure. If the
aim is to identify nodes that can quickly disseminate infor-
mation throughout the network, Closeness is the most critical
measure. Finally, to identify nodes that shape the network’s
overall behavior, PageRank is the most important measure.
It is often useful to calculate all three measures to gain a
comprehensive understanding of the network’s structure and
dynamics.
This representation makes it easier to visualize how the
main research topics are organized in deepfakes research.
Just the most representative topics, corresponding to the most
used keywords, are shown. And they are more prominent the
more present they are in the SDO21 dataset. Highly related
topics, because they are covered jointly in many papers, are
shown closer. This makes it also possible to apply a clustering
algorithm that helps identify the main research topics and
Table 2 Top 5 keywords by co-occurrence (Betweenness)
Node Cluster Betweenness
Deep learning 5 334.138
Convolutional neural networks 2 172.340
Face recognition 3 123.595
Detection methods 5 85.786
Computer vision 3 68.919
Table 3 Top 5 keywords by co-occurrence (Closeness)
Node Cluster Closeness
Deep learning 5 0.0164
Convolutional neural networks 2 0.015
Detection methods 5 0.0144
Face recognition 3 0.014
Digital forensics 2 0.013
Table 4 Top 5 keywords by co-occurrence (PageRank)
Node Cluster PageRank
Deep learning 5 0.105
Convolutional neural networks 2 0.066
Detection methods 5 0.059
Face recognition 3 0.054
Adversarial networks 2 0.040
the central keywords giving the name to the corresponding
topics:
•Red: deep learning, adversarial networks, learning sys-
tems, etc.
•Blue: face recognition, detection methods, forgery detec-
tions, etc.
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Deepfakes: evolution... 11299
Fig. 3 Co-keywords graph representing the main research topics in deepfakes research
•Green: convolutional neural networks, deep neural net-
works, etc.
•Orange: computer vision, digital forensics, etc.
•Purple: social media, video recording, social networking,
etc.
3.2 Trends and evolution of deepfakes research
In this section, we address the third and fourth research ques-
tions, RQ3: Which are the trends in deepfakes research?
and RQ4: How do topics in deepfakes research change over
time?. Despite the short time interval under study, the SDO21
dataset includes records from 2018 to 2021, it is possible to
observe the evolution of the main research topics and identify
their trends.
First of all, after applying a clustering algorithm to the
keywords as detailed in the previous section, we can do more
than just highlight the main topics of the deepfakes research
domain. Each topic can be represented on a plot called The-
matic Map (Cobo et al. 2011) as shown in Fig.4.
This kind of plot classifies the cluster of keywords from the
co-keyword network obtained in the previous section accord-
ing to Callon’s centrality and density measures (Callon et al.
1991):
•Centrality: measures the strength of the links to other
topics, considering those from keywords included in a
cluster to keywords in other clusters. Thus, it measures
the importance of a topic in the context of the whole field
of study.
•Density: is related to the strength of internal links among
all keywords corresponding to the same topic cluster. It
is interpreted as a measure of the topic’s development
degree.
Centrality and Density define the two axes of the Thematic
Map and are used to divide it into four regions. The topics in
these regions are associated with the following trends:
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11300 R. Gil et al.
Fig. 4 Thematic Map the topic trends in deepfakes research. From top-left to bottom-right: Niche, Motor, Emerging, and Basic topics
Fig. 5 This is the keywords plus graph. The colors represent the different clusters: deep learning (red), convolutional neural networks (green),
computer vision (orange), and detection methods (blue)
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Deepfakes: evolution... 11301
•Niche Topics (upper-left quadrant): well-developed inter-
nal ties but unimportant external ties and so, they have
a marginal role in the development of the research field
under study. Non-central but dense.
•Motor Topics (upper-right quadrant): these topics are
both well-developed and important in the context of the
analyzed records. High centrality and density.
•Emerging or Declining Topics (lower-left quadrant):they
are both weakly developed and marginal, with low cen-
trality and density.
•Basic Topics (lower-right quadrant): they are important
for a research field but are not developed, i.e., they show
high centrality but low density.
The Motor and Basic topics are considered those that favor
the development and consolidation of a research field due to
their density and/or centrality. For the particular case of deep-
fakes research captured by the SDO21 dataset, there is a lack
of clear Motor Topics. Most of them are Basic Topics related
to the core of technologies used for deepfakes development,
as is the case of convolutional neural networks or deep neural
networks. This is also the case with detection methods such
as facial recognition.
The only topics that are partially classified as Motor Top-
ics, and thus are computer graphics, network architecture,
and digital forensics. This seems related to the fact that, as
noted at the beginning of Section 3.1,therearetworeviews
on the particular topic of forensics in the last four years.
On the other hand, the topics partially related to Emerging
Topics (declining seems unfeasible given the youth of the
discipline and the short time range) are those associated with
artificial intelligence, data security, and adversarial networks.
Finally, the more mature topics, though apart from the main
efforts in this research domain, are those that have to do with
the analysis in time and frequency to achieve better returns
such as video recording or social networks.
It is important to note that what is being classified into
these different trends are the keywords associated with the
papers. Thus, quite related topics that might be even equiv-
alent in some contexts, like “deep neural networks” and
“neural networks,” might be classified in different quadrants
based on their use in the analyzed literature. The approach is
thus completely agnostic regarding the interpretation of these
keywords because they are highly contextual, like in the case
of neural networks methods and applications (Samek et al.
2021).
In addition to the static view provided by the Thematic
Map in Fig. 4, it is also possible to get an idea of the underly-
ing dynamics using the Thematic Evolution diagram shown
in Fig.5. Thematic Maps for different periods are computed
to identify topics’ evolution over time. Topics at a particular
period are then connected with those in the following one to
create a stream of topics’ evolution. Linking among topics
is based on the percentage of keywords shared between the
identified topics at each period. This way, it is possible to
observe how initial topics might remain partially and split
into other topics that then include the corresponding key-
words.
For the SDO21 dataset, just two time periods have been
defined given the short period, 2018–2020 and 2021. On the
left of Fig.5, there are the topics for the 2018–2020 period,
including computer vision or computer graphics among oth-
ers. On the right side, are those for 2021. The evolution of the
topics is illustrated through the links connecting them, which
are weighted based on the number of keywords shared by the
topics in different periods.
For instance, the computer vision topic has split into many
different ones in 2021, partially remaining as the same topic
but less relevant because many of the associated keywords
are now tied to other topics like deep learning, convolutional
neural networks, or digital forensics. On the other hand, top-
ics like computer graphics have disappeared and now the
associated keywords are contributing to the digital foren-
sics one, which has emerged from keywords from this topic
combined with some from computer vision. Overall, Fig.5
highlights the topics getting traction in deepfakes research
and how they are consolidating from the topics that attracted
the most attention just some years ago.
3.3 Deepfake technologies usage and funding
Regarding the last research questions, RQ5: Who is research-
ing deepfakes? and RQ6: Who is funding deepfakes research?,
they are addressed by analyzing the intellectual and social
structures of the SDO21 dataset. First of all, and as can be
observed in Table 5, the most relevant papers come from
conferences, concretely from IEEE conferences and work-
shops. Forensics, signal processing, law, and blockchain are
among the topics dealt with by the most cited articles about
deepfakes research in Scopus between 2018 and 2021.
Going beyond this superficial analysis, the whole commu-
nity that has generated the papers in SDO21 should be taken
into account. It is for this reason that we have also carried out
an analysis of the social structure to highlight how authors or
institutions related to others in this particular research field.
First of all through a co-authorship network, which is dis-
played in Fig.6.
Many of the most referenced authors in Table 5can be also
identified in the co-authorship network, which also focuses
on the most prominent authors. These authors appear in little
clusters, like Amerini or Agarwal and their corresponding
co-authors. This highlights that even highly cited authors’
work collaborates in relatively closed circles and the overall
community is quite fragmented from this perspective.
If we switch from individual researchers to their insti-
tutions and countries, we can also unveil the underlying
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11302 R. Gil et al.
Table 5 The first 10 most cited articles in SDO21 between 2018 and 2021
Title Publisher Citations References
FaceForensics++: Learning to Detect Manipulated Facial Images IEEE 302 Rossler et al. (2019)
Exposing Deep Fakes Using Inconsistent Head Poses IEEE 165 Yang et al. (2019a)
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations IEEE 136 Matern et al. (2019a)
Protecting World Leaders Against Deep Fakes IEEE 105 Agarwal et al. (2019a)
Combating Deepfake Videos Using Blockchain and Smart Contracts IEEE 80 Hasan and Salah (2019a)
Deep Fakes: A Looming Challenge for Privacy California Law 72 Chesney and Citron (2018)
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics IEEE 64 Li et al. (2020d)
Detecting and Simulating Artifacts in GAN Fake Images IEEE 49 Zhang et al. (2019b)
Media Forensics and DeepFakes: An Overview IEEE 47 Verdoliva (2020)
Deepfake Video Detection through Optical Flow Based CNN IEEE 44 Amerini et al. (2019a)
Fig. 6 Co-authorship network
showing some of the more
prominent authors in SDO21
social structures at these levels. Looking at the corresponding
author countries, shown in Fig.7, we can observe the great
leadership that researchers from China have in this particular
research area.
This is even more evident when we realize that, despite
it might seem that part of this leadership comes from col-
laborations with other countries because it is the country
with the highest amount of inter-country collaborations, these
collaborations are really with Chinese researchers based in
other countries. This is illustrated in Fig.8, which shows
the connection between researchers and countries, and then
from countries to research topics. Therefore, although inter-
country collaboration is indeed very high in China, it is
because these researchers work in other countries, in most
cases in the USA as shown in Fig.8.
Finally, focusing on RQ6: Who is funding deepfakes
research?, the main research funding organization of the
reviewed publications is the National Natural Science (Foun-
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Deepfakes: evolution... 11303
Fig. 7 Corresponding author’s countries, including intra-country (SCP) and inter-country (MCP) collaborations in SDO21
dation of China) with 30 publications, followed by the
Defense Advanced Research Projects Agency (US DARPA)
with 22, and the National Key Research and Development
Program of China with 13. Then, with 12 publications, we
find the US Air Force Research Laboratory and the US
National Science Foundation. A complete table with the top
10 funding organizations is shown in Table 6. Therefore,
China is leading the investigation as a country, mostly from
institutions related to the military and defense sectors. And
as shown in Fig.9, which displays the collaborations among
institutions, these collaborations are kept at the national level.
4 Discussion
This paper employs metadata analysis to investigate the
trends and tendencies related to deepfake research. It is
important to note that our objective was not to conduct a lit-
erature review, but to analyze its metadata. However, it may
be valuable to include this section in the paper that provides
further insights into the representative results of the included
publications.
Deepfakes is a field of research that has gained significant
attention in recent years due to its potential implications in
manipulating digital media. Following the content found in
the lower-right quadrant of Fig.4, which contains “topics that
are important for the research field but are not yet fully devel-
oped” learning systems, detection methods, and algorithms
are the key and future directions in the topic. One of the most
common approaches used in Deepfakes is generative adver-
sarial networks (GANs) (Hu et al. 2021). These techniques
consist of two neural networks, one that generates fake data
and another that evaluates the generated data authenticity.
The results obtained using GANs have shown remarkable
progress in generating highly realistic images and videos.
Another popular method is the use of autoencoders (Singh
et al. 2021), neural networks that are trained to reconstruct
the input data. The encoded representation of the input is
then used to generate new data. The results obtained using
autoencoders have shown promise in generating high-quality
Deepfakes.
In addition to GANs and autoencoders, there are other
methods that have been used in Deepfakes, such as varia-
tional autoencoders (Zendran and Rusiecki 2021), deep belief
networks (Iacobucci et al. 2021), and convolutional neural
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11304 R. Gil et al.
Fig. 8 Relationships among the most prominent researchers, the countries where they conduct research, and the main research topics per country
Fig. 9 Collaboration among
institutions as derived from the
records in the SDO21 dataset
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Deepfakes: evolution... 11305
networks (Agrawal and Sharma 2021). Each of these methods
has shown varying degrees of success in generating Deep-
fakes. Of course, these methods are improving by applying
not only new approaches but combining known techniques
in a new way, as Zheng et al. (2018) proposes a novel two-
stage training process for deep convolutional neural networks
(CNNs) that improves their generalization ability by implicit
regularization, particularly when the training data is limited.
Practical cross-area applications can be found in works
like (Yao et al. 2021), where a method is proposed to auto-
matically separate compound figures in biomedical research
articles. It uses a deep learning model that is trained to sep-
arate the subfigures based on their visual features and is
augmented with a “side loss” to ensure that the model also
considers the context and layout of the subfigures. This arti-
cle is a good example of how a single publication can show
insights into distant topics from upper-left Fig.4(frequency
domain analysis) and lower-right (detection methods) at the
same time.
Despite the progress made in deepfakes, there are still lim-
itations to the current state of the art. The primary challenges
are the ability to generate realistic and high-quality deep-
fakes without significant artifacts (Matern et al. 2019b) and
paradoxically, the ability to detect and prevent the spread of
deepfakes in the public domain (Rossler et al. 2019).
Finally, regarding funding, the top five funding institutions
are either government agencies (NSFC, DARPA, AFRL, and
NSF) or state-sponsored programs (NKRDPC and USNCF)
that prioritize funding for research projects that are strategi-
cally important to their respective countries (see Table 6). As
these projects may include those with military applications
or those that promote the development of key industries, it is
reasonable to infer that these strategic priorities may account
for the low inter-country collaboration ratio (MCP) presented
in Fig. 7. This could be because research with strategic impor-
tance often challenges collaboration due to national security
concerns, funding restrictions (in some cases, funds may be
restricted for international collaborations), and intellectual
property issues.
5 Conclusions
It has been found that growth since 2018 has skyrocketed
regarding research publications in the area of deepfakes. The
queries for Web of Science and Scopus did not retrieve any
results before 2018 but accumulated 311 results, after less
than four years, in 2021. The specific findings for each of the
research questions are discussed in the next paragraphs.
RQ1: What are the main research areas of the articles
in deepfakes? Deepfakes research includes many different
research areas. Our analysis identified 10 different areas with
at least 2% of the articles about the topic. All 10 combined
represent roughly 95% of the papers. However, there is a
big imbalance as just 3 of them accumulate almost 70% of
the results. Computer Science is the most represented with
40.8%, followed by Engineering (19,5%). Thus, these tech-
nological research areas are those with the biggest percentage
of articles. The third area is Social Sciences (9,4%), so deep-
fakes research is also noticeable in social sciences-related
topics.
RQ2: What are the main current topics in deepfakes
research and how are they related? Regarding the most stud-
ied topics, a knowledge discovery approach has been applied
to identify the underlying conceptual structure starting from
the keywords associated with the analyzed articles. Using
a clustering algorithm, five main sets of topics have been
identified, being the most representative topics in each clus-
ter: deep learning, face recognition, convolutional neural
networks, computer vision, and social media. Other rele-
vant topics in each cluster are presented in Fig.3. As can
be observed, overall, deep learning stands out. And more
specifically, adversarial and convolutional neural networks.
It is also relevant to the research on forgery detection and the
literature related to face recognition.
RQ3: Which are the trends in deepfakes research? The
main topics identified using clustering have been analyzed
using a Thematic Map, shown in Fig.4.Thiskindofplot
classifies the clusters of keywords obtained in the previous
section according to Callon’s centrality and density measures
(Callon et al. 1991). Based on these measures, we can iden-
tify:
•Niche Topics: well-developed but with a marginal role in
the development of the research field, like Social Media
related to Video Recording or Neural Networks in the
context of Frequency Domain Analysis.
•Emerging or Declining Topics: these are weakly devel-
oped and still marginal topics. Given the youth of the
deepfakes discipline, they should be mainly emerging
topics. Though the analysis does not identify clear emerg-
ing topics, research related to adversarial networks in the
context of security might be considered an emerging area
with potential relevance in the future.
•Motor Topics: these are both well-developed and impor-
tant in the context of deepfakes. As previously stated,
the youth of the discipline causes a lack of clear candi-
dates. Just topics related to computer graphics, network
architecture, and digital forensics might be classified as
Motor.
•Basic Topics: these are the topics on which research
should be focused. They are important for deepfake
research but have not been developed yet. Here, we can
find the bulk of the research. The most promising top-
ics are convolutional deep neural networks and detection
methods based on face recognition or deep learning.
123
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11306 R. Gil et al.
Table 6 Top 10 Research
Funding Organizations Institution Funded Projects
National Natural Science (Foundation of China) 30
Defense Advanced Research Projects Agency, DARPA 22
National Key Research and Development Program of China 13
Air Force Research Laboratory, AFRL 12
US National Science Foundation 12
Google 5
Nvidia 4
Ministry of Science and ICT, South Korea, MSIT 4
Ministero dell’Istruzione, dell’Università e della Ricerca 4
National Research Foundation of Korea, NRF 4
RQ4: How do topics in deepfakes research change over
time? In addition to the dynamics of deepfakes research cap-
tured by the previous trends analysis, it is also possible to
visualize the underlying dynamics using a Thematic Evolu-
tion chart, as shown in Fig.5. We use Thematic Maps for
different periods, which are then connected with those in the
following one to create a stream of topics’ evolution based
on the percentage of keywords shared between the identi-
fied topics at each period. An insight that can be derived
from this diagram is the diversification of the research around
deep learning, which remains one of the main topics but with
clear applications to texture analysis, fake detection, or online
social networking. The same can be said about computer
vision, which gets out of the main focus even more than deep
learning. On the contrary, from a technical perspective, con-
volutional neural networks are getting more attention from
recent research compared to the beginning of the analyzed
period.
RQ5: Who is researching deepfakes? and RQ6: Who is
funding deepfakes research? It is China as a country the one
that directs the investigations, being the one that contributes
the most in all regards, including funding through the Natu-
ral Science Foundation of China and NKRDPC. Researchers
are mainly from this country, though many of them perform
their research in the USA. On the other hand, the collabo-
ration communities in this research area are still small and
fragmented as observed when studying the co-authorship net-
work. Usually, they are formed by just 2 or 3 authors, except
for the most prolific Chinese researchers that are organized in
a community of 6 authors. The same happens at the country
level, most collaborations are among institutions of the same
country. Additionally, though authors might be based on cen-
ters in different countries, we do not observe inter-country
collaborations.
In addition to the conclusions regarding the different
research questions, we have identified some missing research
topics that we think should already be in the literature, such
as research on the repercussions of deepfakes on marketing
or online negotiation processes. These kinds of risks have
been tangentially addressed in the context of studies about
identity usurpation, which have been the topic of some law
journals. In any case, we believe that considering the emerg-
ing risks of deepfakes in connection with tasks like online
meetings is crucial.
As a limitation of this work, the number of articles found
on deepfakes research made it impossible to perform a sys-
tematic literature review or meta-analysis on the whole area
of deepfakes research. On the other hand, this type of study
can be carried out by focusing on more specific aspects of
the area identified by this work, such as the different arti-
ficial intelligence techniques used to synthesize or analyze
deepfakes.
To conclude, the research articles retrieved about deep-
fakes serve to provide a complete overview of deepfakes. The
main insights of this work include the various areas in which
deepfakes research is being conducted, focusing on which
areas are emerging, those that are considered basic, and those
that currently have the greatest potential for development.
The most studied topics in deepfakes research, including the
various artificial intelligence methods employed, are ana-
lyzed together with emerging and niche topics, to provide
insight into the current trends.
The relationships among the most prominent researchers,
together with the countries in which deepfakes research is
conducted and the main funding sources, complete the out-
look regarding the people who carry out research in that
area and the options for collaboration and obtaining exist-
ing funds.
123
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Deepfakes: evolution... 11307
Overall, this article discusses current trends and opportu-
nities in deepfakes research for practitioners and researchers
interested in this field. Future research directions emerging
from the review point in the direction of the identified “Basic
Topics": convolutional deep neural networks and detection
methods based on face recognition or deep learning.
Supplementary Information The online version contains supplemen-
tary material available at https://doi.org/10.1007/s00500-023-08605-
y.
Author Contributions Rosa Gil and Juan-Miguel López-Gil were
involved in conceptualization and methodology; Jordi Virgili-Gomà
and Roberto García helped in data curation; Roberto García contributed
to funding acquisition; Jordi Virgili-Gomà was involved in validation;
Rosa Gil helped in visualization; Rosa Gil, Juan-Miguel López-Gil,
and Roberto García contributed to writing—original draft; Jordi Virgili-
Gomà, Rosa Gil, Juan-Miguel López-Gil, and Roberto García helped
in writing—review & editing.
Funding Open Access funding provided thanks to the CRUE-CSIC
agreement with Springer Nature. This work has been partially supported
by the project “ANGRU: Applying kNowledge Graphs to research data
ReUsability” with reference PID2020-117912RB-C22 and funded by
MCIN/AEI/10.13039/501100011033. Additionally, this research ben-
efits from funding from the Research Group program of the University
of the Basque Country under contract GIU21/037.
Data Availability The datasets generated and analyzed during the
current study are available online at https://drive.google.com/file/d/
1Attj4yMnsYJB1rx9kYIdVVoQeMhqhW7k/view and are in the pro-
cess of being published in the CORA-RDR repository, https://dataverse.
csuc.cat.
Declarations
Competing Interests The authors have no relevant financial or non-
financial interests to disclose.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indi-
cate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence,
unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the
permitted use, you will need to obtain permission directly from the copy-
right holder. To view a copy of this licence, visit http://creativecomm
ons.org/licenses/by/4.0/.
Appendix A References in SDO21 Dataset
The following table lists all references in the SDO21 dataset
of records retrieved from Scopus as detailed in Sect.1.They
are divided into 4 clusters centered on the keywords associ-
ated with each of them.
Cluster References
deep learning 27.2%
detection methods
18.5% face
recognition 18.5%
Li et al. (2020c), Dang et al. (2020), Ciftci
et al. (2020), Lyu (2020), Nguyen et al.
(2021), Carlini and Farid (2020),
Tursman et al. (2020), Kaur et al.
(2020), Masi et al. (2020), Feng et al.
(2020), Maksutov et al. (2020), Amerini
et al. (2019b), Javed et al. (2021), Kuang
et al. (2021), Patil et al. (2021), Megías
et al. (2021), Patil and Chouragade
(2021), Xu et al. (2021b), Jiang et al.
(2021), Bonomi et al. (2021), Shang
et al. (2021), Ling et al. (2021), Fung
et al. (2021), England et al. (2021),
Fazheng et al. (2021), Zhao et al.
(2021b), Valenzuela et al. (2021), Xiang
et al. (2021), Hosler et al. (2021),
Caldelli et al. (2021), Pan et al. (2021),
Khalil and Maged (2021), Demir and
Ciftci (2021), Li and Lyu (2021), Whler
and Zembaty (2021), Kohli and Gupta
(2021), Lv (2021), Dondero (2021),
Guo et al. (2021), Carter et al. (2021),
Tu et al. (2021), Shelke and Kasana
(2021), Pokroy and Egorov (2021), Tjon
et al. (2021), Sun et al. (2021),
Sankaranarayanan et al. (2021), Li et al.
(2021a), Yang et al. (2021b), Kawa and
Syga (2021), Gong et al. (2021),
Hussain et al. (2021), Godulla et al.
(2021), Tolosana et al. (2021), Jeong
et al. (2021), Hernandez-Ortega et al.
(2021), Zhang et al. (2021a), Deshmukh
and Wankhade (2021), Caporusso
(2021), Amerini and Caldelli (2020),
Zhu et al. (2020a), Bonettini et al.
(2020), Kukanov et al. (2020), Bondi
et al. (2020), Nasar et al. (2020), Mittal
et al. (2020a), Du et al. (2020a), El Rai
et al. (2020), Ramadhani and Munir
(2020), Gupta et al. (2020), Du et al.
(2020b), Lewis et al. (2020), Chugh
et al. (2020), Shah et al. (2020), Tarasiou
and Zafeiriou (2020), Ross et al. (2020),
Nguyen and Derakhshani (2020),
Khodabakhsh and Loiselle (2020), Li
et al. (2020e), Zotov et al. (2020), Wu
et al. (2020a), Hongmeng et al. (2020),
Chowdhury and Lubna (2020), Suratkar
et al. (2020a), Hewage and Ekmekcioglu
(2020), Zhao et al. (2020b), Younus and
Hasan (2020a), Alattar et al. (2020), Li
et al. (2020a), Peng et al. (2020), Ivanov
et al. (2020), Zhao et al. (2020a),
Cozzolino et al. (2019)
123
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11308 R. Gil et al.
Cluster References
deep learning 11.3%
adversarial networks
7.5% artificial
intelligence 6.6%
Kietzmann et al. (2020), Ahmed et al.
(2021), Jung et al. (2020), Wang et al.
(2020a), Mirsky and Lee (2021),
Meskys et al. (2020a), Chesney and
Citron (2019), Zhang et al. (2020), Fallis
(2020), Maddocks (2020), Farish
(2020a), Fletcher (2018a), Rao et al.
(2021), Khormali and Yuan (2021), Lai
and Patrick Rau (2021), Yu et al. (2021),
Pavis (2021), Lees et al. (2021), de Seta
(2021), Bode et al. (2021), Holliday
(2021), Mihailova (2021), Bode (2021),
Ayers (2021), Hayward and Maas
(2021), Ahmed (2021c), Kim et al.
(2021a), Sybrandt and Safro (2021),
Diakopoulos and Johnson (2021), José
and García-Ull (2021), Huber et al.
(2021), Tahir and Batool (2021), Nygren
et al. (2021), Fagni et al. (2021), Medoff
and B.K. (2021), Pu et al. (2021b),
Castillo Camacho and Wang (2021b),
Mcglynn and Johnson (2021),
D’Alessandra and Sutherland (2021),
Freeman (2021), Karasavva and
Noorbhai (2021a), Brooks (2021),
Iacobucci et al. (2021), Hancock and
Bailenson (2021a), Johnson (2021),
Chora´setal.(2021), Tesfagergish et al.
(2021), O’Donnell (2021), da Silva
(2021), Thaw et al. (2021), Frick et al.
(2021), Aboueldahab and Freixo
(2021a), Hänska (2021), Zhang et al.
(2021b), de Ruiter (2021), Ahmed
(2021b), Murphy and Flynn (2021),
Zhao et al. (2021a), Wahl-Jorgensen and
Carlson (2021), Pavlíková et al. (2021),
Dasilva et al. (2021), Vizoso et al.
(2021), Johnson and Diakopoulos
(2021), Chi et al. (2021), Kietzmann
et al. (2021), Dobber et al. (2021),
Kwok and Koh (2021), Kikerpill (2020),
Perot and Mostert (2020a), Hasan and
Salah (2019a), Aliman and Kester
(2020), Xie et al. (2020), Pan et al.
(2020), Kozyreva et al. (2020),
Partadiredja et al. (2020), Tulk Jesso
et al. (2020), Colon (2020a), Gosse and
Burkell (2020), Lomnitz et al. (2020),
Wang et al. (2020b), Katarya and Lal
(2020), Kaye and Johnson (2020),
Gandhi and Jain (2020), Chang et al.
(2020), Šepec and Lango (2020), Hosier
and Stamm (2020), Hartmann and Giles
(2020), Gong et al. (2020), Jongman
(2020), Pertsch et al. (2020), Shahar and
Hel-Or (2020), Houde et al. (2020), Zhu
et al. (2020b), Jeong (2020), Amelin and
Channov (2020), Kang and Park (2020),
Pashentsev (2020), Davis and Fors
(2020), Hazan (2020), Bore (2020)
Cluster References
deep learning 27.7%
convolutional neural
networks 23.1%
detection methods
16.9%
Yang et al. (2019b), Jiang et al. (2020),
Agarwal et al. (2019b), Agarwal et al.
(2020a),Mittaletal.(2020b), Zi et al.
(2020), Agarwal et al. (2020b), Chen
et al. (2020), Montserrat et al. (2020),
Ahmed (2021a), Marcon et al. (2021),
Ajoy et al. (2021), Liang and Deng
(2021), Ru et al. (2021), Tran et al.
(2021), Ismail et al. (2021), Yavuzkilic
et al. (2021), Fei et al. (2021), Yang et al.
(2021a), Siegel et al. (2021), Agarwal
and Farid (2021), Masood et al. (2021),
Singh et al. (2021), Sanghvi et al. (2021),
Xu et al. (2021a), Agrawal and Sharma
(2021), Zendran and Rusiecki (2021),
Trinh et al. (2021), Li et al. (2021b), Lu
et al. (2021), Su et al. (2021), Luo et al.
(2021), Biswas et al. (2021), Jiang et al.
(2021), Korshunov and Marcel (2021),
Chen and Tan (2021), Jin et al. (2021),
Khalil et al. (2021), Gu et al. (2021),
Yang et al. (2021c), Chintha et al.
(2020a), Younus and Hasan (2020b),
Korshunov and Marcel (2019), Suratkar
et al. (2020b), Mitra et al. (2020), Liang
et al. (2020), Burroughs et al. (2020),
Huang et al. (2020a), Chintha et al.
(2020b), Ki Chan et al. (2020), Zhu et al.
(2020c), Yang et al. (2020), Wu et al.
(2020b), Jafar et al. (2020), ¸Sener (2020),
Malolan et al. (2020), Fernandes and Jha
(2020), Pantserev (2020a), Wang et al.
(2020c), Zeng et al. (2020), Pantserev
(2020b), Karandikar et al. (2020),
Megahed and Han (2020), Albahar and
Almalki (2019), Sohrawardi et al. (2019)
deep learning 36.4%
adversarial networks
22. 7% computer
vision 20.5%
Li et al. (2020b), Tolosana et al. (2020),
Verdoliva (2020), Rossler et al. (2019),
Matern et al. (2019b), Prajwal et al.
(2020), Khalid and Woo (2020), Neves
et al. (2020), Fernandes et al. (2020),
Yang et al. (2021d), Swathi and Saritha
(2021), Amerini et al. (2021a), Agarwal
et al. (2021), Dal Cortivo et al. (2021),
Schwarcz and Chellappa (2021), Kim
et al. (2021b), Bailer et al. (2021), Tariq
et al. (2021), Hu et al. (2021), Ahmed and
Sonuç (2021), Laishram et al. (2021),
Goebel et al. (2021), Han and Gevers
(2021), Echizen et al. (2021), Fernando
et al. (2021), Kubanek et al. (2021),
Huang et al. (2020b), Baek et al. (2020),
Zhang et al. (2019c), Pu et al. (2020),
Pham et al. (2020), Wang and Dantcheva
(2020), Mi et al. (2020), Ranjan et al.
(2020), Frank et al. (2020), Yang and
Lim (2020), Hashmi et al. (2020),
Ranjith Kumar et al. (2020), Xuan et al.
(2019), Guan et al. (2019), Kharbat et al.
(2019), Bose and Aarabi (2019), Zhang
et al. (2019a), Ward (2019)
123
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Deepfakes: evolution... 11309
References
Aboueldahab S, Freixo I (2021) App-generated evidence: a promis-
ing tool for international criminal justice? Int Crim Law Rev
21(3):505–533. https://doi.org/10.1163/15718123-bja10061
Agarwal S, Farid H (2021) Detecting deep-fake videos from aural and
oral dynamics. In: IEEE Computer Society Conference on Com-
puter Vision and Pattern Recognition Workshops, pp 981–989,
https://doi.org/10.1109/CVPRW53098.2021.00109
Agarwal S, Farid H, El-Gaaly T, et al. (2020a) Detecting deep-fake
videos from appearance and behavior. In: 2020 IEEE International
Workshop on Information Forensics and Security, WIFS 2020,
https://doi.org/10.1109/WIFS49906.2020.9360904
Agarwal S, Farid H, Fried O, et al. (2020b) Detecting deep-fake videos
from phoneme-viseme mismatches. In: IEEE Computer Society
Conference on Computer Vision and Pattern Recognition Work-
shops, pp 2814–2822, https://doi.org/10.1109/CVPRW50498.
2020.00338
Agarwal S, Farid H, Gu Y, et al. (2019a) Protecting world leaders against
deep fakes. pp 38–45, conference of 32nd IEEE/CVF Conference
on Computer Vision and PatternRecognition Workshops, CVPRW
2019 ; Conference Date: 16 June 2019 Through 20 June 2019;
Conference Code:159074
Agarwal S, Farid H, Gu Y, et al. (2019b) Protecting world leaders against
deep fakes. In: IEEE Computer Society Conference on Computer
Vision and Pattern Recognition Workshops, pp 38–45
Agarwal H, Singh A, Rajeswari D (2021) Deepfake detection using svm.
In: Proceedings of the 2nd International Conference on Electronics
and Sustainable Communication Systems, ICESC 2021, pp 1245–
1249, https://doi.org/10.1109/ICESC51422.2021.9532627
Agrawal R, Sharma D (2021) A survey on video-based fake news
detection techniques. In: Proceedings of the 2021 8th Interna-
tional Conference on Computing for Sustainable Global Devel-
opment, INDIACom 2021, pp 663–669, https://doi.org/10.1109/
INDIACom51348.2021.00117
Ahmed S (2021) Fooled by the fakes: cognitive differences in per-
ceived claim accuracy and sharing intention of non-political
deepfakes. Personal Individ Differ. https://doi.org/10.1016/j.paid.
2021.111074
Ahmed S (2021) Navigating the maze: deepfakes, cognitive ability, and
social media news skepticism. New Media Soc. https://doi.org/10.
1177/14614448211019198
Ahmed S (2021) Who inadvertently shares deepfakes? analyzing the
role of political interest, cognitive ability, and social network size.
Telemat Inform. https://doi.org/10.1016/j.tele.2020.101508
Ahmed S, Sonuç E (2021) Deepfake detection using rationale-
augmented convolutional neural network. Appl Nanosci (Switzer-
land). https://doi.org/10.1007/s13204-021-02072-3
Ahmed M, Miah M, Bhowmik A, et al. (2021) Awareness to deepfake: A
resistance mechanism to deepfake. In: 2021 International Congress
of Advanced Technology and Engineering, ICOTEN 2021, https://
doi.org/10.1109/ICOTEN52080.2021.9493549
Ajoy A, Mahindrakar C, Gowrish D, et al. (2021) Deepfake detection
using a frame based approach involving cnn. In: Proceedings of
the 3rd International Conference on Inventive Research in Com-
puting Applications, ICIRCA 2021, pp 1329–1333, https://doi.
org/10.1109/ICIRCA51532.2021.9544734
Alattar A, Sharma R, Scriven J (2020) A system for mitigating the
problem of deepfake news videos using watermarking. In: Adnan
M. A.M. GGNasir D. N.D. (ed) IS and T International Symposium
on Electronic Imaging Science and Technology, https://doi.org/10.
2352/ISSN.2470-1173.2020.4.MWSF-117
Albahar M, Almalki J (2019) Deepfakes: threats and countermeasures
systematic review. J Theor Appl Inf Technol 97(22):3242–3250
Aliman NM, Kester L (2020) Malicious design in aivr, falsehood and
cybersecurity-oriented immersive defenses. In: Proceedings - 2020
IEEE International Conference on Artificial Intelligence and Vir-
tual Reality, AIVR 2020, pp 130–137, https://doi.org/10.1109/
AIVR50618.2020.00031
Amelin R, Channov S (2020) On the legal issues of face processing
technologies. Commun Comput Inf Sci 1242:223–236. https://doi.
org/10.1007/978-3-030- 65218-0_17
Amerini I, Anagnostopoulos A, Maiano L et al (2021) Deep learning
for multimedia forensics. Found Trends Comput Gr Vis 12(4):309–
457. https://doi.org/10.1561/0600000096
Amerini I, Caldelli R (2020) Exploiting prediction error inconsistencies
through lstm-based classifiers to detect deepfake videos. In: IH
and MMSec 2020 - Proceedings of the 2020 ACM Workshop on
Information Hiding and Multimedia Security, pp 97–102, https://
doi.org/10.1145/3369412.3395070
Amerini I, Galteri L, Caldelli R, et al. (2019a) Deepfake video detection
through optical flow based cnn. In: 2019 IEEE/CVF Interna-
tional Conference on Computer Vision Workshop (ICCVW).
IEEE Computer Society, Los Alamitos, CA, USA, pp 1205–
1207, https://doi.org/10.1109/ICCVW.2019.00152,https://doi.
ieeecomputersociety.org/10.1109/ICCVW.2019.00152
Amerini I, Galteri L, Caldelli R, et al. (2019b) Deepfake video detection
through optical flow based cnn. In: Proceedings - 2019 Interna-
tional Conference on Computer Vision Workshop, ICCVW 2019,
pp 1205–1207, https://doi.org/10.1109/ICCVW.2019.00152
Aria M, Cuccurullo C (2017) bibliometrix: an r-tool for comprehensive
science mapping analysis. J Informetr 11(4):959–975. https://doi.
org/10.1016/j.joi.2017.08.007
Ayers D (2021) The limits of transactional identity: whiteness
and embodiment in digital facial replacement. Convergence
27(4):1018–1037. https://doi.org/10.1177/13548565211027810
Baek JY, Yoo YS, Bae SH (2020) Generative adversarial ensemble
learning for face forensics. IEEE Access 8:45,421-45,431. https://
doi.org/10.1109/ACCESS.2020.2968612
Bailer W, Thallinger G, Backfried G, et al. (2021) Challenges for
automatic detection of fake news related to migration : Invited
paper. In: Proceedings - 2021 IEEE International Conference
on Cognitive and Computational Aspects of Situation Man-
agement, CogSIMA 2021, pp 133–138, https://doi.org/10.1109/
CogSIMA51574.2021.9475929
Biswas A, Bhattacharya D, Kumar K (2021) Deepfake detection using
3d-xception net with discrete fourier transformation. J Inf Syst
Telecommun 9(35):161–168
Bode L (2021) Deepfaking keanu: youtube deepfakes, platform visual
effects, and the complexity of reception. Convergence 27(4):919–
934. https://doi.org/10.1177/13548565211030454
Bode L, Lees D, Golding D (2021) The digital face and deepfakes
on screen. Convergence 27(4):849–854. https://doi.org/10.1177/
13548565211034044
Bondi L, Daniele Cannas E, Bestagini P, et al. (2020) Training strategies
and data augmentations in cnn-based deepfake video detection. In:
2020 IEEE International Workshop on Information Forensics and
Security, WIFS 2020, https://doi.org/10.1109/WIFS49906.2020.
9360901
Bonettini N, Bondi L, Cannas E, et al. (2020) Video face manipulation
detection through ensemble of cnns. In: Proceedings - International
Conference on Pattern Recognition, pp 5012–5019, https://doi.
org/10.1109/ICPR48806.2021.9412711
Bonomi M, Pasquini C, Boato G (2021) Dynamic texture analysis for
detecting fake faces in video sequences. J Vis Commun Image
Represent. https://doi.org/10.1016/j.jvcir.2021.103239
Bore J (2020) Insider threat. Adv Sci Technol Secur Appl. https://doi.
org/10.1007/978-3-030- 35746-7_19
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11310 R. Gil et al.
Bose A, Aarabi P (2019) Virtual fakes: Deepfakes for virtual reality. In:
IEEE 21st International Workshop on Multimedia Signal Process-
ing, MMSP 2019, https://doi.org/10.1109/MMSP.2019.8901744
Brooks C (2021) Popular discourse around deepfakes and the inter-
disciplinary challenge of fake video distribution. Cyberpsychol
Behav Soc Netw 24(3):159–163. https://doi.org/10.1089/cyber.
2020.0183
Burroughs S, Gokaraju B, Roy K, et al. (2020) Deepfakes detection
in videos using feature engineering techniques in deep learning
convolution neural networkframeworks. In: Proceedings - Applied
Imagery Pattern Recognition Workshop, https://doi.org/10.1109/
AIPR50011.2020.9425347
Caldelli R, Galteri L, Amerini I et al (2021) Optical flow based cnn for
detection of unlearnt deepfake manipulations. Pattern Recognit
Lett 146:31–37. https://doi.org/10.1016/j.patrec.2021.03.005
Callon M, Courtial JP, Laville F (1991) Co-word analysis as a tool for
describing the network of interactions between basic and techno-
logical research: The case of polymer chemsitry. Scientometrics
22(1):155–205. https://doi.org/10.1007/BF02019280
Caporusso N (2021) Deepfakes for the good: a beneficial application
of contentious artificial intelligence technology. Adv Intell Syst
Comput. https://doi.org/10.1007/978-3-030-51328-3_33
Carlini N, Farid H (2020) Evading deepfake-imagedetectors with white-
and black-box attacks. In: IEEE Computer Society Conference on
Computer Vision and Pattern Recognition Workshops, pp 2804–
2813, https://doi.org/10.1109/CVPRW50498.2020.00337
Carter M, Tsikerdekis M, Zeadally S (2021) Approaches for fake con-
tent detection: strengths and weaknesses to adversarial attacks.
IEEE Internet Comput 25(2):73–83. https://doi.org/10.1109/MIC.
2020.3032323
Castillo Camacho I, Wang K (2021) A comprehensive review of deep-
learning-based methods for image forensics. J Imaging. https://
doi.org/10.3390/jimaging7040069
Castillo Camacho I, Wang K (2021) A comprehensive review of deep-
learning-based methods for image forensics. J Imaging. https://
doi.org/10.3390/jimaging7040069
Chang X, Wu J, Yang T, et al. (2020) Deepfake face image detection
based on improved vgg convolutional neural network. In: Fu J. SJ
(eds). Chinese Control Conference, CCC, pp 7252–7256, https://
doi.org/10.23919/CCC50068.2020.9189596
Chawla R (2019) Deepfakes: how a pervert shook the world. Int J Adv
Res Dev 4(6):4–8
Chen B, Tan S (2021) Featuretransfer: Unsupervised domain adaptation
for cross-domain deepfake detection. Security and Communica-
tion Networks. https://doi.org/10.1155/2021/9942754
Chen P, Liu J, Liang T, et al. (2020) Fsspotter: Spotting face-swapped
video by spatial and temporal clues. In: Proceedings - IEEE Inter-
national Conference on Multimedia and Expo, https://doi.org/10.
1109/ICME46284.2020.9102914
Chesney RM, Citron DK (2018) Deep fakes: a looming challenge for
privacy, democracy, and national security. Calif Law Rev107:1753
Chesney B, Citron D (2019) Deep fakes: A looming challenge
for privacy, democracy, and national security. Calif Law Rev
107(6):1753–1820. https://doi.org/10.15779/Z38RV0D15J
Chi H, Maduakor U, Alo R et al (2021) Integrating deepfake detection
into cybersecurity curriculum. Adv Intell Syst Comput 1288:588–
598. https://doi.org/10.1007/978-3-030-63128-4_45
Chintha A, Thai B, Sohrawardi S et al (2020) Recurrent convolutional
structures for audio spoof and video deepfake detection. IEEE J
Sel Top Signal Process 14(5):1024–1037. https://doi.org/10.1109/
JSTSP.2020.2999185
Chintha A, Rao A, Sohrawardi S, et al. (2020a) Leveraging edges
and optical flow on faces for deepfake detection. In: IJCB 2020 -
IEEE/IAPR International Joint Conference on Biometrics, https://
doi.org/10.1109/IJCB48548.2020.9304936
Cho M, Jeong Y (2017) Face recognition performance comparison
between fake faces and live faces.Soft Comput 21(12):3429–3437.
https://doi.org/10.1007/s00500-015-2019-4
Chora´s M, Demestichas K, Giełczyk A et al (2021) Advanced machine
learning techniques for fake news (online disinformation) detec-
tion: a systematic mapping study. Appl Soft Comput. https://doi.
org/10.1016/j.asoc.2020.107050
Chowdhury S, Lubna J (2020) Review on deep fake: A looming tech-
nological threat. In: 2020 11th International Conference on Com-
puting, Communication and Networking Technologies, ICCCNT
2020, https://doi.org/10.1109/ICCCNT49239.2020.9225630
Chugh K, Gupta P, Dhall A, et al. (2020) Not made for each other-
audio-visual dissonance-based deepfake detection and localiza-
tion. In: MM 2020 - Proceedings of the 28th ACM International
Conference on Multimedia, pp 439–447, https://doi.org/10.1145/
3394171.3413700
Ciftci U, Demir I, Yin L (2020) How do the hearts of deep fakes beat?
deep fake source detection via interpreting residuals with bio-
logical signals. In: IJCB 2020 - IEEE/IAPR International Joint
Conference on Biometrics, https://doi.org/10.1109/IJCB48548.
2020.9304909
Cobo M, López-Herrera A, Herrera-Viedma E et al (2011) Science
mapping software tools: review, analysis, and cooperative study
among tools. J Am Soc Inf Sci Technol 62(7):1382–1402. https://
doi.org/10.1002/asi.21525
Colon M (2020) How can iowans effectively prevent the commercial
misappropriation of their identities? why iowa needs a right of
publicity statute. Iowa Law Rev 106(1):411–454
Cozzolino D, Poggi G, Verdoliva L (2019) Extracting camera-based
fingerprints for video forensics. In: IEEE Computer Society Con-
ference on Computer Vision and Pattern Recognition Workshops,
pp 130–137
da Silva R (2021) Updating the authentication of digital evidence in the
international criminal court. Int Crim Law Rev. https://doi.org/10.
1163/15718123-bja10083
Dal Cortivo D, Mandelli S, Bestagini P et al (2021) Cnn-based multi-
modal camera model identification on video sequences. J Imaging.
https://doi.org/10.3390/jimaging7080135
D’Alessandra F, Sutherland K (2021) The promise and challenges of
new actors and new technologies in international justice. J Int Crim
Justice 19(1):9–34. https://doi.org/10.1093/jicj/mqab034
Dang H, Liu F, Stehouwer J, et al. (2020) On the detection of digital
face manipulation. In: Proceedings of the IEEE Computer Soci-
ety Conference on Computer Vision and Pattern Recognition, pp
5780–5789, https://doi.org/10.1109/CVPR42600.2020.00582
Dasilva J, Ayerdi K, Galdospin T (2021) Deepfakes on twitter: which
actors control their spread? Media Commun 9(1):301–312. https://
doi.org/10.17645/MAC.V9I1.3433
Davis M, Fors P (2020) Towards a typology of intentionally inaccurate
representations of reality in media content. IFIP Adv Inf Com-
mun Technol 590:291–304. https://doi.org/10.1007/978-3-030-
62803-1_23
de Ruiter A (2021) The distinct wrong of deepfakes. Philos Technol.
https://doi.org/10.1007/s13347-021-00459-2
de Seta G (2021) Huanlian, or changing faces: Deepfakes on chinese
digital media platforms. Convergence 27(4):935–953. https://doi.
org/10.1177/13548565211030185
Demir I, Ciftci U (2021) Where do deep fakes look? synthetic face detec-
tion via gaze tracking. In: S.N. S (eds) Eye Tracking Research
and Applications Symposium (ETRA), https://doi.org/10.1145/
3448017.3457387
Deshmukh A, WankhadeS (2021) Deepfake detection approaches using
deep learning: a systematic review. Lect Notes Netw Syst 146:293–
302. https://doi.org/10.1007/978-981-15-7421-4_27
Diakopoulos N, Johnson D (2021) Anticipating and addressing
the ethical implications of deepfakes in the context of elec-
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Deepfakes: evolution... 11311
tions. New Media Soc 23(7):2072–2098. https://doi.org/10.1177/
1461444820925811
Dobber T, Metoui N, Trilling D et al (2021) Do (microtargeted) deep-
fakes have real effects on political attitudes? Int J Press/Polit
26(1):69–91. https://doi.org/10.1177/1940161220944364
Dondero M (2021) Composition and decomposition in artistic portraits,
scientific photography, and deep fake videos1. Lexia 2021(37–
38):439–454. https://doi.org/10.4399/978882553853321
Du C, Duong L, Trung H, et al. (2020a) Efficient-frequency: A
hybrid visual forensic framework for facial forgery detection. In:
2020 IEEE Symposium Series on Computational Intelligence,
SSCI 2020, pp 707–712, https://doi.org/10.1109/SSCI47803.
2020.9308305
Du M, Pentyala S, Li Y, et al. (2020b) Towards generalizable deep-
fake detection with locality-aware autoencoder. In: International
Conference on Information and Knowledge Management, Pro-
ceedings, pp 325–334, https://doi.org/10.1145/3340531.3411892
Echizen I, Babaguchi N, Yamagishi J et al (2021) Generation and
detection of media clones. IEICE Trans Inf Syst E104D(1):12–
23. https://doi.org/10.1587/transinf.2020MUI0002
El Rai M, Al Ahmad H, Gouda O, et al. (2020) Fighting deep-
fake by residual noise using convolutional neural networks.
In: 2020 3rd International Conference on Signal Processing
and Information Security, ICSPIS 2020, https://doi.org/10.1109/
ICSPIS51252.2020.9340138
England P, Malvar H, Horvitz E, et al. (2021) Amp: Authentication of
media via provenance. In: MMSys 2021 - Proceedings of the 2021
Multimedia Systems Conference, pp 109–121, https://doi.org/10.
1145/3458305.3459599
Fagni T, Falchi F, Gambini M et al (2021) Tweepfake: about detect-
ing deepfake tweets. PLoS ONE. https://doi.org/10.1371/journal.
pone.0251415
Fallis D (2020) The epistemic threat of deepfakes. Philos Technol.
https://doi.org/10.1007/s13347-020-00419-2
Farish K (2020) Do deepfakes pose a golden opportunity? considering
whether english law should adopt california’s publicity right in the
age of the deepfake. J Intell Prop Law Pract 15(1):40–48. https://
doi.org/10.1093/jiplp/jpz139
Fazheng W, Yanwei Y, Shuiyuan D, et al. (2021) Research on loca-
tion of chinese handwritten signature based on efficientdet. In:
2021 IEEE 4th International Conference on Big Data and Arti-
ficial Intelligence, BDAI 2021, pp 192–198, https://doi.org/10.
1109/BDAI52447.2021.9515222
Fei J, Xia Z, Yu P et al (2021) Exposing ai-generated videos
with motion magnification. Multimed Tools Appl 80(20):30,789-
30,802. https://doi.org/10.1007/s11042-020-09147-3
Feng D, Lu X, Lin X (2020) Deep detection for face manipulation.
Commun Comput Inf Sci 1333:316–323. https://doi.org/10.1007/
978-3-030-63823- 8_37
Fernandes S, Jha S (2020) Adversarial attack on deepfake detection
using rl based texture patches. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics) 12535 LNCS:220–235. https://
doi.org/10.1007/978-3-030- 66415-2_14
Fernandes S, Raj S, Ewetz R, et al. (2020) Detecting deepfake
videos using attribution-based confidence metric. In: IEEE Com-
puter Society Conference on Computer Vision and Pattern
Recognition Workshops, pp 1250–1259, https://doi.org/10.1109/
CVPRW50498.2020.00162
Fernando T, Fookes C, Denman S et al (2021) Detection of fake
and fraudulent faces via neural memory networks. IEEE Trans
Inf Forensics Secur 16:1973–1988. https://doi.org/10.1109/TIFS.
2020.3047768
Fletcher J (2018) Deepfakes, artificial intelligence, and some kind of
dystopia: the new faces of online post-fact performance. Theatre J
70(4):455–471. https://doi.org/10.1353/tj.2018.0097
Frank J, Eisenhofer T, Schönherr L, et al. (2020) Leveraging frequency
analysis for deep fake image recognition. In: Daume H. SA (eds)
37th International Conference on Machine Learning, ICML 2020,
pp 3205–3216
Freeman L (2021) Weapons of war, tools of justice: using artificial
intelligence to investigate international crimes. J Int Crim Justice
19(1):35–53. https://doi.org/10.1093/jicj/mqab013
Frick R, Zmudzinski S, Steinebach M (2021) Detecting deepfakes with
haralick’s texture properties. In: Adnan M. A.M. GGNasir D.
N.D. (eds) IS and T International Symposium on Electronic Imag-
ing Science and Technology, https://doi.org/10.2352/ISSN.2470-
1173.2021.4.MWSF-271
Fung S, Lu X, Zhang C, et al. (2021) Deepfakeucl: Deepfake detection
via unsupervised contrastive learning. In: Proceedings of the Inter-
national Joint Conference on Neural Networks, https://doi.org/10.
1109/IJCNN52387.2021.9534089
Gandhi A, Jain S (2020) Adversarial perturbations fool deepfake
detectors. In: Proceedings of the International Joint Conference
on Neural Networks, https://doi.org/10.1109/IJCNN48605.2020.
9207034
Godulla A, Hoffmann C, Seibert D (2021) Dealing with deepfakes - an
interdisciplinary examination of the state of research and impli-
cations for communication studies [der umgang mit deepfakes -
eine interdisziplinäre untersuchung zum forschungsstand und imp-
likationen für die kommunikationswissenschaft]. Stud Commun
Media 10(1):73–96. https://doi.org/10.5771/2192-4007-2021-1-
72
Goebel M, Nataraj L, Nanjundaswamy T, et al. (2021) Detection, attri-
bution and localization of gan generated images. In: Adnan M.
A.M. GGNasir D. N.D. (eds) IS and T International Symposium
on Electronic Imaging Science and Technology, https://doi.org/
10.2352/ISSN.2470-1173.2021.4.MWSF-276
Gong D, Goh O, Kumar Y et al (2020) Deepfake forensics, an
ai-synthesized detection with deep convolutional generative adver-
sarial networks. Int J Adv Trends Comput Sci Eng 9(3):2861–2870.
https://doi.org/10.30534/ijatcse/2020/58932020
Gong D, Kumar Y, Ye O et al (2021) Deepfakenet, an efficient deepfake
detection method. Int J Adv Comput Sci Appl 12(6):201–207.
https://doi.org/10.14569/IJACSA.2021.0120622
Gosse C, Burkell J (2020) Politics and porn: how news media character-
izes problems presented by deepfakes. Crit Stud Media Commun
37(5):497–511. https://doi.org/10.1080/15295036.2020.1832697
Guan H, Kozak M, Robertson E, et al. (2019) Mfc datasets: Large-scale
benchmark datasets for media forensic challenge evaluation. In:
Proceedings - 2019 IEEE Winter Conference on Applications of
Computer Vision Workshops, WACVW 2019, pp 63–72, https://
doi.org/10.1109/WACVW.2019.00018
Guo Z, Yang G, Chen J et al (2021) Fake face detection via adap-
tive manipulation traces extraction network. Comput Vis Image
Underst. https://doi.org/10.1016/j.cviu.2021.103170
Gupta P, Chugh K, Dhall A, et al. (2020) The eyes know it: Fakeet-
an eye-tracking database to understand deepfake perception. In:
ICMI 2020 - Proceedings of the 2020 International Conference
on Multimodal Interaction, pp 519–527, https://doi.org/10.1145/
3382507.3418857
Gu Y, Zhao X, Gong C, et al. (2021) Deepfake video detection using
audio-visual consistency. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics) 12617 LNCS:168–180. https://
doi.org/10.1007/978-3-030- 69449-4_13
Hancock J, Bailenson J (2021) The social impact of deepfakes.
Cyberpsychol Behav Soc Netw 24(3):149–152. https://doi.org/10.
1089/cyber.2021.29208.jth
Han J, Gevers T (2021) Mmd based discriminative learning for face
forgery detection. Lecture Notes in Computer Science (includ-
ing subseries Lecture Notes in Artificial Intelligence and Lecture
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11312 R. Gil et al.
Notes in Bioinformatics) 12626 LNCS:121–136. https://doi.org/
10.1007/978-3-030-69541-5_8
Hänska M (2021). Communication against domination: Ideas of justice
from the printing press to algorithmic media. https://doi.org/10.
4324/9780429280795
Hartmann K, Giles K (2020) The next generation of cyber-enabled infor-
mation warfare. In: International Conference on Cyber Conflict,
CYCON, pp 233–250, https://doi.org/10.23919/CyCon49761.
2020.9131716
Hasan H, Salah K (2019) Combating deepfake videos using blockchain
and smart contracts. IEEE Access 7:41,596-41,606. https://doi.
org/10.1109/ACCESS.2019.2905689
Hashmi M, Ashish B, Keskar A et al (2020) An exploratory anal-
ysis on visual counterfeits using conv-lstm hybrid architec-
ture. IEEE Access 8:101,293-101,308. https://doi.org/10.1109/
ACCESS.2020.2998330
Hayward K, Maas M (2021) Artificial intelligence and crime: a primer
for criminologists. Crime Media Cult 17(2):209–233. https://doi.
org/10.1177/1741659020917434
Hazan S (2020) Deep fake and cultural truth - custodians of cultural
heritage in the age of a digital reproduction. Lecture Notes in
Computer Science (including subseries Lecture Notes in Arti-
ficial Intelligence and Lecture Notes in Bioinformatics) 12215
LNCS:65–80. https://doi.org/10.1007/978-3-030-50267-6_6
Hernandez-Ortega J, Tolosana R, Fierrez J, et al. (2021) Deepfakeson-
phys: Deepfakes detection based on heart rate estimation. In:
CEUR Workshop Proceedings
Hewage C, Ekmekcioglu E (2020) Multimedia quality of experience
(qoe): current status and future direction. Future Internet. https://
doi.org/10.3390/FI12070121
Higgins JP, Thomas J, Chandler J et al (2019) Cochrane handbook for
systematic reviews of interventions. John Wiley & Sons
Holliday C (2021) Rewriting the stars: surface tensions and
gender troubles in the online media production of digital
deepfakes. Convergence 27(4):899–918. https://doi.org/10.1177/
13548565211029412
Hongmeng Z, Zhiqiang Z, Lei S, et al. (2020) A detection method for
deepfake hard compressed videos based on super-resolution recon-
struction using cnn. In: ACM International Conference Proceeding
Series, pp 98–103, https://doi.org/10.1145/3409501.3409542
Hosier B, Stamm M (2020) Detecting video speed manipulation. In:
IEEE Computer Society Conference on Computer Vision and Pat-
tern Recognition Workshops, pp 2860–2869, https://doi.org/10.
1109/CVPRW50498.2020.00343
Hosler B, Salvi D, Murray A, et al. (2021) Do deepfakes feel emo-
tions? a semantic approach to detecting deepfakes via emotional
inconsistencies. In: IEEE Computer Society Conference on Com-
puter Vision and Pattern Recognition Workshops, pp 1013–1022,
https://doi.org/10.1109/CVPRW53098.2021.00112
Houde S, Liao V, Martino J, et al. (2020) Business (mis)use cases of
generative ai. In: Geyer W. SSMKhazaeni Y. (ed) CEUR Workshop
Proceedings
Huang R, Fang F, Nguyen H, et al. (2020a) Security of facial forensics
models against adversarial attacks. In: Proceedings - International
Conference on Image Processing, ICIP, pp 2236–2240, https://doi.
org/10.1109/ICIP40778.2020.9190678
Huang Y, Juefei-Xu F, Wang R, et al. (2020b) Fakepolisher: Making
deepfakes more detection-evasive by shallow reconstruction. In:
MM 2020 - Proceedings of the 28th ACM International Conference
on Multimedia, pp 1217–1226, https://doi.org/10.1145/3394171.
3413732
Huber E, Pospisil B, Haidegger W (2021) Modus operandi in fake
news : Invited paper. In: Proceedings - 2021 IEEE International
Conference on Cognitive and Computational Aspects of Situa-
tion Management, CogSIMA 2021, pp 127–132, https://doi.org/
10.1109/CogSIMA51574.2021.9475926
Hu S, Li Y, Lyu S (2021) Exposing gan-generated faces using inconsis-
tent corneal specular highlights. In: ICASSP, IEEE International
Conference on Acoustics, Speech and Signal Processing - Pro-
ceedings, pp 2500–2504, https://doi.org/10.1109/ICASSP39728.
2021.9414582
Hussain S, Neekhara P, Jere M, et al. (2021) Adversarial deep-
fakes: Evaluating vulnerability of deepfake detectors to adversarial
examples. In: Proceedings - 2021 IEEE Winter Conference on
Applications of Computer Vision, WACV 2021, pp 3347–3356,
https://doi.org/10.1109/WACV48630.2021.00339
Iacobucci S, De Cicco R, Michetti F et al (2021) Deepfakes unmasked:
the effects of information priming and bullshit receptivity on deep-
fake recognition and sharing intention. Cyberpsychol Behav Soc
Netw 24(3):194–202. https://doi.org/10.1089/cyber.2020.0149
Ismail A, Elpeltagy M, Zaki M et al (2021) A new deep learning-based
methodology for video deepfake detection using xgboost. Sensors.
https://doi.org/10.3390/s21165413
Ivanov N, Arzhskov A, Ivanenko V (2020) Combining deep learning
and super-resolution algorithms for deep fake detection. In: S. S
(ed) Proceedings of the 2020 IEEE Conference of Russian Young
Researchers in Electrical and Electronic Engineering, EICon-
Rus 2020, pp 326–328, https://doi.org/10.1109/EIConRus49466.
2020.9039498
Jafar M, Ababneh M, Al-Zoube M, et al. (2020) Digital forensics and
analysis of deepfake videos. In: 2020 11th International Confer-
ence on Information and Communication Systems, ICICS 2020,
pp 53–58, https://doi.org/10.1109/ICICS49469.2020.239493
Javed A, Jalil Z, Zehra W et al (2021) A comprehensivesurvey on digital
video forensics: Taxonomy, challenges, and future directions. Eng
Appl Artif Intell. https://doi.org/10.1016/j.engappai.2021.104456
Jeong D (2020) Artificial intelligence security threat, crime, and foren-
sics: taxonomy and open issues. IEEE Access 8:184,560-184,574.
https://doi.org/10.1109/ACCESS.2020.3029280
Jeong Y, Choi J, Kim D, et al. (2021) Dofnet: Depth of field difference
learning for detecting image forgery. Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelli-
gence and Lecture Notes in Bioinformatics) 12627 LNCS:83–100.
https://doi.org/10.1007/978-3-030-69544-6_6
Jiang L, Li R, Wu W, et al. (2020) Deeperforensics-1.0: A large-
scale dataset for real-world face forgery detection. In: Proceedings
of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition, pp 2886–2895, https://doi.org/10.1109/
CVPR42600.2020.00296
Jiang J, Wang B, Li B, et al. (2021) Practical face swapping detection
based on identity spatial constraints. In: 2021 IEEE International
Joint Conference on Biometrics, IJCB 2021, https://doi.org/10.
1109/IJCB52358.2021.9484396
Jin X, Ye D, Chen C (2021) Countering spoof: towards detecting deep-
fake with multidimensional biological signals. Secur Commun
Netw. https://doi.org/10.1155/2021/6626974
Johnson J (2021) ‘catalytic nuclear war’ in the age of artificial intelli-
gence & autonomy: emerging military technology and escalation
risk between nuclear-armed states. J Strateg Stud. https://doi.org/
10.1080/01402390.2020.1867541
Johnson D, Diakopoulos N (2021) What to do about deepfakes. Com-
mun ACM 64(3):33–35. https://doi.org/10.1145/3447255
Jongman B (2020) Recent online resources for the analysis of terrorism
and related subjects. Perspect Terror 14(1):155–190
José F, García-Ull GU (2021) Deepfakes: the next challenge in
fake news detection. Analisi 64:103–120. https://doi.org/10.5565/
REV/ANALISI.3378
Jung T, Kim S, Kim K (2020) Deepvision: deepfakes detection
using human eye blinking pattern. IEEE Access 8:83,144-83,154.
https://doi.org/10.1109/ACCESS.2020.2988660
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Deepfakes: evolution... 11313
Kang M, Park J (2020) Contragan: Contrastive learning for conditional
image generation. In: Advances in Neural Information Processing
Systems
Karandikar A, Deshpande V, Singh S et al (2020) Deepfake video detec-
tion using convolutional neural network. Int J Adv Trends Comput
Sci Eng 9(2):1311–1315. https://doi.org/10.30534/ijatcse/2020/
62922020
Karasavva V, Noorbhai A (2021) The real threat of deepfake pornogra-
phy: a review of canadian policy. Cyberpsychol Behav Soc Netw
24(3):203–209. https://doi.org/10.1089/cyber.2020.0272
Katarya R, Lal A (2020) A study on combating emerging threat of
deepfake weaponization. In: Proceedings of the 4th International
Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC
2020, pp 485–490, https://doi.org/10.1109/I-SMAC49090.2020.
9243588
Kaur S, Kumar P, Kumaraguru P (2020) Deepfakes: temporal sequential
analysis to detect face-swapped video clips using convolutional
long short-term memory. J Electron Imaging. https://doi.org/10.
1117/1.JEI.29.3.033013
Kawa P, Syga P (2021) Verify it yourself: A note on activationfunctions’
influence on fast deepfake detection. In: di Vimercati S.De.C. SP
(ed) Proceedings of the 18th International Conference on Security
and Cryptography, SECRYPT 2021, pp 779–784, https://doi.org/
10.5220/0010581707790784
Kaye B, Johnson T (2020) Appsolutely trustworthy? perceptions of trust
and bias in mobile apps. Atl J Commun 28(4):257–271. https://doi.
org/10.1080/15456870.2020.1720023
Khalid H, Woo S (2020) Oc-fakedect: Classifying deepfakes using one-
class variational autoencoder. In: IEEE Computer Society Confer-
ence on Computer Vision and Pattern Recognition Workshops, pp
2794–2803, https://doi.org/10.1109/CVPRW50498.2020.00336
Khalil S, Youssef S, Saleh S (2021) Article icaps-dfake: an integrated
capsule-based model for deepfake image and video detection.
Future Internet. https://doi.org/10.3390/fi13040093
Khalil H, Maged S (2021) Deepfakes creation and detection using deep
learning. In: 2021 International Mobile, Intelligent, and Ubiqui-
tous Computing Conference, MIUCC 2021, pp 24–27, https://doi.
org/10.1109/MIUCC52538.2021.9447642
Kharbat F, Elamsy T, Mahmoud A, et al. (2019) Image feature detec-
tors for deepfake video detection. In: Proceedings of IEEE/ACS
International Conference on Computer Systems and Applications,
AICCSA, https://doi.org/10.1109/AICCSA47632.2019.9035360
Khodabakhsh A, Loiselle H (2020) Action-independent generalized
behavioral identity descriptors for look-alike recognition in videos.
In: BIOSIG 2020 - Proceedings of the 19th International Confer-
ence of the Biometrics Special Interest Group
Khormali A, Yuan JS (2021) Add: Attention-based deepfake detection
approach. Big Data Cognitive Comput. https://doi.org/10.3390/
bdcc5040049
Ki Chan C, Kumar V, Delaney S, et al. (2020) Combating deepfakes:
Multi-lstm and blockchain as proof of authenticity for digital
media. In: 2020 IEEE / ITU International Conference on Artificial
Intelligence for Good, AI4G 2020, pp 55–62, https://doi.org/10.
1109/AI4G50087.2020.9311067
Kietzmann J, Lee L, McCarthy I et al (2020) Deepfakes: trick or treat?
Bus Horiz 63(2):135–146. https://doi.org/10.1016/j.bushor.2019.
11.006
Kietzmann J, Mills A, Plangger K (2021) Deepfakes: perspectiveson the
future reality of advertising and branding. Int J Advert 40(3):473–
485. https://doi.org/10.1080/02650487.2020.1834211
Kikerpill K (2020) Choose your stars and studs: the rise of deep-
fake designer porn. Porn Studies 7(4):352–356. https://doi.org/
10.1080/23268743.2020.1765851
Kim KS, Sin SC, Yoo-Lee E (2021) Use and evaluation of information
from social media: a longitudinal cohort study. Libr Inf Sci Res.
https://doi.org/10.1016/j.lisr.2021.101104
Kim M, Tariq S, Woo S (2021b) Fretal: Generalizing deepfake detection
using knowledge distillation and representation learning. In: IEEE
Computer Society Conference on Computer Vision and Pattern
Recognition Workshops, pp 1001–1012, https://doi.org/10.1109/
CVPRW53098.2021.00111
Kohli A, Gupta A (2021) Detecting deepfake, faceswap and
face2face facial forgeries using frequency cnn. Multimed Tools
Appl 80(12):18,461-18,478. https://doi.org/10.1007/s11042-020-
10420-8
Korshunov P, Marcel S (2019) Vulnerability assessment and detection of
deepfake videos. In: 2019 International Conference on Biometrics,
ICB 2019, https://doi.org/10.1109/ICB45273.2019.8987375
Korshunov P, Marcel S (2021) Subjective and objective evaluation of
deepfake videos. In: ICASSP, IEEE International Conference on
Acoustics, Speech and Signal Processing - Proceedings, pp 2510–
2514, https://doi.org/10.1109/ICASSP39728.2021.9414258
Kozyreva A, Lewandowsky S, Hertwig R (2020) Citizens versus the
internet: confronting digital challenges with cognitive tools. Psy-
chol Sci Public Interest 21(3):103–156. https://doi.org/10.1177/
1529100620946707
Kuang Z, Guo Z, Fang J et al (2021) Unnoticeable synthetic face replace-
ment for image privacy protection. Neurocomputing 457:322–333.
https://doi.org/10.1016/j.neucom.2021.06.061
Kubanek M, Bartłomiejczyk K, Bobulski J (2021) Detection of artifi-
cial images and changes in real images using convolutional neural
networks. Advances in Intelligent Systems and Computing 1267
AISC:197–207. https://doi.org/10.1007/978-3-030-57805-3_19
Kukanov I, Karttunen J, Sillanpaa H, et al. (2020) Cost sensitive opti-
mization of deepfake detector. In: 2020 Asia-Pacific Signal and
Information Processing Association Annual Summit and Confer-
ence, APSIPA ASC 2020 - Proceedings, pp 1300–1303
Kwok A, Koh S (2021) Deepfake: a social construction of technology
perspective. Curr Issue Tour 24(13):1798–1802. https://doi.org/
10.1080/13683500.2020.1738357
Lai X, Patrick Rau PL (2021) Has facial recognition technology been
misused? a user perception model of facial recognition scenarios.
Comput Hum Behav. https://doi.org/10.1016/j.chb.2021.106894
Laishram L, Rahman M, Jung S (2021) Challenges and applications of
face deepfake. Commun Comput Inf Sci 1405:131–156. https://
doi.org/10.1007/978-3-030- 81638-4_11
Lees D, Bashford-Rogers T, Keppel-Palmer M (2021) The digital res-
urrection of margaret thatcher: creative, technological and legal
dilemmas in the use of deepfakes in screen drama. Convergence
27(4):954–973. https://doi.org/10.1177/13548565211030452
Lewis J, Toubal I, Chen H, et al. (2020) Deepfake video detection
based on spatial, spectral, and temporal inconsistencies using
multimodal deep learning. In: Proceedings - Applied Imagery Pat-
tern Recognition Workshop, https://doi.org/10.1109/AIPR50011.
2020.9425167
Li H, Li B, Tan S et al (2020) Identification of deep network gener-
ated images using disparities in color components. Signal Process.
https://doi.org/10.1016/j.sigpro.2020.107616
Liang T, Chen P, Zhou G, et al. (2020) Sdhf: Spotting deepfakes with
hierarchical features. In: Alamaniotis M. PS (ed) Proceedings
- International Conference on Tools with Artificial Intelligence,
ICTAI, pp 675–680, https://doi.org/10.1109/ICTAI50040.2020.
00108
Liang J, Deng W (2021) Identifying rhythmic patterns for face forgery
detection and categorization. In: 2021 IEEE International Joint
Conference on Biometrics, IJCB 2021, https://doi.org/10.1109/
IJCB52358.2021.9484400
Li L, Bao J, Zhang T, et al. (2020b) Face x-ray for more general face
forgery detection. In: Proceedings of the IEEE Computer Soci-
ety Conference on Computer Vision and Pattern Recognition, pp
5000–5009, https://doi.org/10.1109/CVPR42600.2020.00505
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11314 R. Gil et al.
Li M, Liu B, Hu Y, et al. (2020c) Exposing deepfake videos by track-
ing eye movements. In: Proceedings - International Conference
on Pattern Recognition, pp 5184–5189, https://doi.org/10.1109/
ICPR48806.2021.9413139
Li M, Liu B, Hu Y, et al. (2021a) Deepfake detection using robust spatial
and temporal features from facial landmarks. In: Proceedings - 9th
International Workshop on Biometrics and Forensics, IWBF 2021,
https://doi.org/10.1109/IWBF50991.2021.9465076
Li Y, Lyu S (2021) Obstructing deepfakes by disrupting face detection
and facial landmarks extraction. Advances in Computer Vision and
Pattern Recognition pp 247–267. https://doi.org/10.1007/978-3-
030-74697-1_12
Ling H, Huang J, Zhao C, et al. (2021) Learning diverse local patterns for
deepfake detection with image-level supervision. In: Proceedings
of the International Joint Conference on Neural Networks, https://
doi.org/10.1109/IJCNN52387.2021.9533912
Li W, Wang Q, Wang R, et al. (2021b) Exposing deepfakes via localiz-
ing the manipulated artifacts. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics) 12919 LNCS:3–20. https://doi.
org/10.1007/978-3-030- 88052-1_1
Li Y, Yang X, Sun P, et al. (2020d) Celeb-df: A large-scale challeng-
ing dataset for deepfake forensics. pp 3204–3213, https://doi.org/
10.1109/CVPR42600.2020.00327, conference of 2020 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, CVPR
2020 ; Conference Date: 14 June 2020 Through 19 June 2020;
Conference Code:162261
Li Y, Yang X, Sun P, et al. (2020e) Celeb-df: A large-scale challenging
dataset for deepfake forensics. In: Proceedings of the IEEE Com-
puter Society Conference on Computer Vision and Pattern Recog-
nition, pp 3204–3213, https://doi.org/10.1109/CVPR42600.2020.
00327
Lomnitz M, Hampel-Arias Z, Sandesara V, et al. (2020) Multi-
modal approach for deepfake detection. In: Proceedings - Applied
Imagery Pattern Recognition Workshop, https://doi.org/10.1109/
AIPR50011.2020.9425192
Lu Y, Liu Y, Fei J et al (2021) Channel-wise spatiotemporal aggregation
technology for face video forensics. Secur Commun Netw. https://
doi.org/10.1155/2021/5524930
Luo Y, Ye F, Weng B et al (2021) A novel defensive strategy for
facial manipulation detection combining bilateral filtering and
joint adversarial training. Secur Commun Netw. https://doi.org/
10.1155/2021/4280328
Lv L (2021) Smart watermark to defend against deepfake image manip-
ulation. In: 2021 IEEE 6th International Conference on Computer
and Communication Systems, ICCCS 2021, pp 380–384, https://
doi.org/10.1109/ICCCS52626.2021.9449287
Lyu S (2020) Deepfake detection: Current challenges and next
steps. In: 2020 IEEE International Conference on Multimedia
and Expo Workshops, ICMEW 2020, https://doi.org/10.1109/
ICMEW46912.2020.9105991
Maddocks S (2020) ‘a deepfake porn plot intended to silence
me’: exploring continuities between pornographic and ‘politi-
cal’ deep fakes. Porn Stud 7(4):415–423. https://doi.org/10.1080/
23268743.2020.1757499
Maksutov A, Morozov V, LavrenovA, et al. (2020) Methods of deepfake
detection based on machine learning. In: S. S (ed) Proceedings
of the 2020 IEEE Conference of Russian Young Researchers in
Electrical and Electronic Engineering, EIConRus 2020, pp 408–
411, https://doi.org/10.1109/EIConRus49466.2020.9039057
Malolan B, Parekh A, Kazi F (2020) Explainable deep-fake detection
using visual interpretability methods. In: Proceedings - 3rd Inter-
national Conference on Information and Computer Technologies,
ICICT 2020, pp 289–293, https://doi.org/10.1109/ICICT50521.
2020.00051
Maras MH, Alexandrou A (2019) Determining authenticity of video
evidence in the age of artificial intelligence and in the wake of
deepfake videos. Int J Evid Proof 23(3):255–262
Marcon F, Pasquini C, Boato G (2021) Detection of manipulated face
videos over social networks: a large-scale study. J Imaging. https://
doi.org/10.3390/jimaging7100193
Masi I, Killekar A, Mascarenhas R, et al. (2020) Two-branch recur-
rent network for isolating deepfakes in videos. Lecture Notes in
Computer Science (including subseries Lecture Notes in Arti-
ficial Intelligence and Lecture Notes in Bioinformatics) 12352
LNCS:667–684. https://doi.org/10.1007/978-3-030-58571-6_39
Masood M, Nawaz M, Javed A, et al. (2021) Classification of deep-
fake videos using pre-trained convolutional neural networks. In:
2021 International Conference on Digital Futures and Trans-
formative Technologies, ICoDT2 2021, https://doi.org/10.1109/
ICoDT252288.2021.9441519
Matern F, Riess C, Stamminger M (2019a) Exploiting visual artifacts to
expose deepfakes and face manipulations. pp 83–92, https://doi.
org/10.1109/WACVW.2019.00020,conference of 19th IEEE Win-
ter Conference on Applications of Computer Vision Workshops,
WACVW 2019 ; Conference Date: 7 January 2019 Through 11
January 2019; Conference Code:145024
Matern F, Riess C, Stamminger M (2019b) Exploiting visual artifacts to
expose deepfakes and face manipulations. In: Proceedings - 2019
IEEE Winter Conference on Applications of Computer Vision
Workshops, WACVW 2019, pp 83–92, https://doi.org/10.1109/
WACVW.2019.00020
Mcglynn C, Johnson K (2021) Cyberflashing: Recognising Harms,
Reforming Laws
Medoff N, B.K. K (2021) Interconnected by the internet. https://doi.
org/10.4324/9781003020721-5
Megahed A, Han Q (2020) Face2face manipulation detection based
on histogram of oriented gradients. In: Proceedings - 2020 IEEE
19th International Conference on Trust, Security and Privacy in
Computing and Communications, TrustCom 2020, pp 1260–1267,
https://doi.org/10.1109/TrustCom50675.2020.00169
Megías D, Kuribayashi M, Rosales A, et al. (2021) Dissimilar: Towards
fake news detection using information hiding, signal processing
and machine learning. In: ACM International Conference Proceed-
ing Series, https://doi.org/10.1145/3465481.3470088
Meskys E, Liaudanskas A, Kalpokiene J et al (2020) Regulating deep
fakes: legal and ethical considerations. J Intell Proper Law Pract
15(1):24–31. https://doi.org/10.1093/jiplp/jpz167
Mi Z, Jiang X, Sun T et al (2020) Gan-generated image detection with
self-attention mechanism against gan generator defect. IEEE J Sel
Top Sign Proces 14(5):969–981. https://doi.org/10.1109/JSTSP.
2020.2994523
Mihailova M (2021) To dally with dalí: deepfake (inter)faces in the art
museum. Convergence 27(4):882–898. https://doi.org/10.1177/
13548565211029401
Mirsky Y, Lee W (2021) The creation and detection of deepfakes. ACM
Comput Surv 54(1):7
Mitra A, Mohanty S, Corcoran P, et al. (2020) A novel machine learning
based method for deepfake video detection in social media. In:
Proceedings - 2020 6th IEEE International Symposium on Smart
Electronic Systems, iSES 2020, pp 91–96, https://doi.org/10.1109/
iSES50453.2020.00031
Mittal T, Bhattacharya U, Chandra R, et al. (2020b) Emotions don’t lie:
An audio-visual deepfake detection method using affective cues.
In: MM 2020 - Proceedings of the 28th ACM International Con-
ference on Multimedia, pp 2823–2832, https://doi.org/10.1145/
3394171.3413570
Mittal H, Saraswat M, Bansal J, et al. (2020a) Fake-face image clas-
sification using improved quantum-inspired evolutionary-based
feature selection method. In: 2020 IEEE Symposium Series on
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Deepfakes: evolution... 11315
Computational Intelligence, SSCI 2020, pp 989–995, https://doi.
org/10.1109/SSCI47803.2020.9308337
Montserrat D, Hao H, Yarlagadda S, et al. (2020) Deepfakes detec-
tion with automatic face weighting. In: IEEE Computer Society
Conference on Computer Vision and Pattern Recognition Work-
shops, pp 2851–2859, https://doi.org/10.1109/CVPRW50498.
2020.00342
Murphy G, Flynn E (2021) Deepfake false memories. Memory. https://
doi.org/10.1080/09658211.2021.1919715
Nasar B, Sajini T, Lason E (2020) Deepfake detection in media files
- audios, images and videos. In: 2020 IEEE Recent Advances
in Intelligent Computational Systems, RAICS 2020, pp 74–79,
https://doi.org/10.1109/RAICS51191.2020.9332516
Neves J, Tolosana R, Vera-Rodriguez R et al (2020) Ganprintr: improved
fakes and evaluation of the state of the art in face manipulation
detection. IEEE J Sel Top Sign Proces 14(5):1038–1048. https://
doi.org/10.1109/JSTSP.2020.3007250
Nguyen X, Tran T, Le V et al (2021) Learning spatio-temporal fea-
tures to detect manipulated facial videos created by the deepfake
techniques. Forensic Sci Int Dig Investig. https://doi.org/10.1016/
j.fsidi.2021.301108
Nguyen H, Derakhshani R (2020) Eyebrow recognition for identify-
ing deepfake videos. In: BIOSIG 2020 - Proceedings of the 19th
International Conference of the Biometrics Special Interest Group
Nygren T, Guath M, Axelsson CA et al (2021) Combatting visual fake
news with a professional fact-checking tool in education in france,
romania, spain and sweden. Information (Switzerland). https://doi.
org/10.3390/info12050201
O’Donnell N (2021) Have we no decency? section 230 and the liability
of social media companies for deepfake videos. Univ Ill Law Rev
2021(3):701–740
Pan Z, Ren Y, Zhang X (2021) Low-complexity fake face detec-
tion based on forensic similarity. Multimed Syst 27(3):353–361.
https://doi.org/10.1007/s00530-021-00756-y
Pan D, Sun L, Wang R, et al. (2020) Deepfake detection through
deep learning. In: Proceedings - 2020 IEEE/ACM International
Conference on Big Data Computing, Applications and Tech-
nologies, BDCAT 2020, pp 134–143, https://doi.org/10.1109/
BDCAT50828.2020.00001
Pantserev K (2020a) Deepfakes as the new challenge of national and
international psychological security. In: F. M (ed) Proceedings of
the European Conference on the Impact of Artificial Intelligence
and Robotics, ECIAIR 2020, pp 93–99, https://doi.org/10.34190/
EAIR.20.003
Pantserev K (2020) The malicious use of ai-based deepfake technology
as the new threat to psychological security and political stability.
Adv Sci Technol Secur Appl. https://doi.org/10.1007/978-3-030-
35746-7_3
Partadiredja R, Serrano C, Ljubenkov D (2020) Ai or human: The
socio-ethical implications of ai-generated media content. In: I. W
(ed) 13th CMI Conference on Cybersecurity and Privacy - Digital
Transformation - Potentials and Challenges, CMI 2020, https://
doi.org/10.1109/CMI51275.2020.9322673
Pashentsev E (2020) Malicious use of deepfakes and political stabil-
ity. In: F. M (ed) Proceedings of the European Conference on the
Impact of Artificial Intelligence and Robotics, ECIAIR 2020, pp
100–107, https://doi.org/10.34190/EAIR.20.025
Patil U, Chouragade P (2021) Deepfake video authentication based on
blockchain. In: Proceedings of the 2nd International Conference
on Electronics and Sustainable Communication Systems, ICESC
2021, pp 1110–1113, https://doi.org/10.1109/ICESC51422.2021.
9532725
Patil U, Chouragade P, Ambhore P (2021) An effective blockchain tech-
nique to resist against deepfake videos. In: Proceedings of the
3rd International Conference on Inventive Research in Comput-
ing Applications, ICIRCA 2021, pp 1646–1652, https://doi.org/
10.1109/ICIRCA51532.2021.9544854
Pavis M (2021) Rebalancing our regulatory response to deepfakes with
performers’ rights. Convergence 27(4):974–998. https://doi.org/
10.1177/13548565211033418
Pavlíková M, Šenkýˇrová B, Drmola J (2021) Propaganda and disinfor-
mation go online. Political Campaigning and Communication pp
43–74. https://doi.org/10.1007/978-3-030-58624-9_2
Peng C, Zhang W, Liu D, et al. (2020) Temporal consistency based deep
face forgery detection network. Lecture Notes in Computer Sci-
ence (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics) 12488 LNCS:55–63. https://
doi.org/10.1007/978-3-030- 62463-7_6
Perot E, Mostert F (2020) Fake it till you make it: an examination of
the us and english approaches to persona protection as applied to
deepfakes on social media. J Intell Proper Law Pract 15(1):32–39.
https://doi.org/10.1093/jiplp/jpz164
Pertsch K, Rybkin O, Ebert F, et al. (2020) Long-horizon visual plan-
ning with goal-conditioned hierarchical predictors. In: Advances
in Neural Information Processing Systems
Pham KL, Dang KM, Tang LP, et al. (2020) Gan generated portraits
detection using modified vgg-16 and efficientnet. In: Bao V.N.Q.
TTVan VuN. (ed) Proceedings - 2020 7th NAFOSTED Conference
on Information and Computer Science, NICS 2020, pp 344–349,
https://doi.org/10.1109/NICS51282.2020.9335837
Pokroy A, Egorov A (2021) Efficientnets for deepfake detection: Com-
parison of pretrained models. In: S. S (ed) Proceedings of the 2021
IEEE Conference of Russian Young Researchers in Electrical and
Electronic Engineering, ElConRus 2021, pp 598–600, https://doi.
org/10.1109/ElConRus51938.2021.9396092
Prajwal K, Mukhopadhyay R, Namboodiri V, et al. (2020) A lip
sync expert is all you need for speech to lip generation in the
wild. In: MM 2020 - Proceedings of the 28th ACM International
Conference on Multimedia, pp 484–492, https://doi.org/10.1145/
3394171.3413532
Pu J, Mangaokar N, Kelly L et al (2021) Deepfake videos in the
wild: analysis and detection. Proceedings of the Web Conference
2021:981–992
Pu J, Mangaokar N, Kelly L, et al. (2021b) Deepfake videos in the
wild: Analysis and detection. In: The Web Conference 2021 - Pro-
ceedings of the World Wide Web Conference, WWW 2021, pp
981–992, https://doi.org/10.1145/3442381.3449978
Pu J, Mangaokar N, Wang B, et al. (2020) Noisescope: Detecting deep-
fake images in a blind setting. In: ACM International Conference
Proceeding Series, pp 913–927, https://doi.org/10.1145/3427228.
3427285
Ramadhani K, Munir R (2020) A comparative study of deepfake video
detection method. In: 2020 3rd International Conference on Infor-
mation and Communications Technology, ICOIACT 2020, pp
394–399, https://doi.org/10.1109/ICOIACT50329.2020.9331963
Ranjan P, Patil S, Kazi F (2020) Improved generalizability of deep-
fakes detection using transfer learning based cnn framework. In:
Proceedings - 3rd International Conference on Information and
Computer Technologies, ICICT 2020, pp 86–90, https://doi.org/
10.1109/ICICT50521.2020.00021
Ranjith Kumar M, Prabhu A, Asthana S et al (2020) Denet: a
deepfake visual media detection network. J Adv Res Dyn
Control Syst 12(2):792–799. https://doi.org/10.5373/JARDCS/
V12I2/S20201098
Rao S, Verma A, Bhatia T (2021) A review on social spam detection:
challenges, open issues, and future directions. Expert Syst Appl.
https://doi.org/10.1016/j.eswa.2021.115742
Ross A, Banerjee S, Chowdhury A (2020) Security in smart cities: a
brief review of digital forensic schemes for biometric data. Pattern
Recogn Lett 138:346–354. https://doi.org/10.1016/j.patrec.2020.
07.009
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11316 R. Gil et al.
Rossler A, Cozzolino D, Verdoliva L, et al. (2019) Faceforensics++:
Learning to detect manipulated facial images. In: Proceedings of
the IEEE International Conference on Computer Vision, pp 1–11,
https://doi.org/10.1109/ICCV.2019.00009
Ru Y, Zhou W, Liu Y, et al. (2021) Bita-net: Bi-temporal attention
network for facial video forgery detection. In: 2021 IEEE Inter-
national Joint Conference on Biometrics, IJCB 2021, https://doi.
org/10.1109/IJCB52358.2021.9484408
Samek W, Montavon G, Lapuschkin S et al (2021) Explaining
deep neural networks and beyond: a review of methods and
applications. Proc IEEE 109(3):247–278. https://doi.org/10.1109/
JPROC.2021.3060483
Sanghvi B, Shelar H, Pandey M, et al. (2021) Detection of machine gen-
erated multimedia elements using deep learning. In: Proceedings
- 5th International Conference on Computing Methodologies and
Communication, ICCMC 2021, pp 1238–1243, https://doi.org/10.
1109/ICCMC51019.2021.9418008
Sankaranarayanan A, Groh M, Picard R, et al. (2021) The presiden-
tial deepfakes dataset. In: Aimeur E. HHDiaz Ferreyra N.E. (ed)
CEUR Workshop Proceedings, pp 57–72
Schwarcz S, Chellappa R (2021) Finding facial forgery artifacts with
parts-based detectors. In: IEEE Computer Society Conference on
Computer Vision and Pattern Recognition Workshops, pp 933–
942, https://doi.org/10.1109/CVPRW53098.2021.00104
¸Sener O (2020) New literacies for disinformation and manipulation
through digital sound and video
Šepec M, Lango M (2020) Virtual revenge pornography as a new online
threat to sexual integrity. Balk Soc Sci Rev 15(15):117–134
Shahar H, Hel-Or H (2020) Fake video detection using facial color.
In: Final Program and Proceedings - IS and T/SID Color Imaging
Conference, pp 175–180, https://doi.org/10.2352/issn.2169-2629.
2020.28.27
Shah Y, Shah P, Patel M, et al. (2020) Deep learning model-based mul-
timedia forgery detection. In: Proceedings of the 4th International
Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC
2020, pp 564–572, https://doi.org/10.1109/I-SMAC49090.2020.
9243530
Shang Z, Xie H, Zha Z et al (2021) Prrnet: pixel-region relation net-
work for face forgery detection. Pattern Recogn. https://doi.org/
10.1016/j.patcog.2021.107950
Shelke N, Kasana S (2021) A comprehensive survey on passive tech-
niques for digital video forgery detection. Multimed Tools Appl
80(4):6247–6310. https://doi.org/10.1007/s11042-020-09974-4
Siegel D, Kraetzer C, Seidlitz S et al (2021) Media forensics considera-
tions on deepfake detection with hand-crafted features. J Imaging.
https://doi.org/10.3390/jimaging7070108
Singh R, Sarda P, Aggarwal S, et al. (2021) Demystifying deep-
fakes using deep learning. In: Proceedings - 5th Interna-
tional Conference on Computing Methodologies and Commu-
nication, ICCMC 2021, pp 1290–1298, https://doi.org/10.1109/
ICCMC51019.2021.9418477
Sohrawardi S, Seng S, Chintha A, et al. (2019) Poster: Towards robust
open-world detection of deepfakes. In: Proceedings of the ACM
Conference on Computer and Communications Security, pp 2613–
2615, https://doi.org/10.1145/3319535.3363269
Su Y, Xia H, Liang Q et al (2021) Exposing deepfake videos using
attention based convolutional lstm network. Neural Process Lett.
https://doi.org/10.1007/s11063-021-10588-6
Sun P, Yan Z, Shen Z, et al. (2021) Deepfakes detection based on multi
scale fusion. Lecture Notes in Computer Science (including sub-
series Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics) 12878 LNCS:346–353. https://doi.org/10.1007/
978-3-030-86608- 2_38
Suratkar S, Johnson E, Variyambat K, et al. (2020a) Employing
transfer-learning based cnn architectures to enhance the gener-
alizability of deepfake detection. In: 2020 11th International Con-
ference on Computing, Communication and Networking Tech-
nologies, ICCCNT 2020, https://doi.org/10.1109/ICCCNT49239.
2020.9225400
Suratkar S, Kazi F, Sakhalkar M, et al. (2020b) Exposing deep-
fakes using convolutional neural networks and transfer learn-
ing approaches. In: 2020 IEEE 17th India Council Inter-
national Conference, INDICON 2020, https://doi.org/10.1109/
INDICON49873.2020.9342252
Swathi P, Saritha S (2021) Deepfake creation and detection:a survey.
In: Proceedings of the 3rd International Conference on Inventive
Research in Computing Applications, ICIRCA 2021, pp 584–588,
https://doi.org/10.1109/ICIRCA51532.2021.9544522
Sybrandt J, Safro I (2021) Cbag: conditional biomedical abstract gener-
ation. PLoS ONE. https://doi.org/10.1371/journal.pone.0253905
Tahir R, Batool B (2021) Seeing is believing: Exploring perceptual
diferences in deepfake videos. In: Conference on Human Fac-
tors in Computing Systems - Proceedings, https://doi.org/10.1145/
3411764.3445699
Tarasiou M, Zafeiriou S (2020) Extracting deep local features to detect
manipulated images of human faces. In: Proceedings - Interna-
tional Conference on Image Processing, ICIP, pp 1821–1825,
https://doi.org/10.1109/ICIP40778.2020.9190714
Tariq S, Lee S, Woo S (2021) One detector to rule them all: Towards a
general deepfake attack detection framework. In: The Web Con-
ference 2021 - Proceedings of the World Wide Web Conference,
WWW 2021, pp 3625–3637, https://doi.org/10.1145/3442381.
3449809
Tesfagergish S, Damaševiˇcius R, Kapoˇci¯ut˙e-Dzikien˙e J (2021) Deep
fake recognition in tweets using text augmentation, word embed-
dings and deep learning. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics) 12954 LNCS:523–538. https://
doi.org/10.1007/978-3-030- 86979-3_37
Thaw N, July T,Wai A et al (2021) How are deepfake videos detected?
an initial user study. Commun Comput Inf Sci 1419:631–636.
https://doi.org/10.1007/978-3-030-78635-9_80
Tjon E, Moh M, Moh TS (2021) Eff-ynet: A dual task network for
deepfake detection and segmentation. In: Lee S. IRChoo H. (ed)
Proceedings of the 2021 15th International Conference on Ubiq-
uitous Information Management and Communication, IMCOM
2021, https://doi.org/10.1109/IMCOM51814.2021.9377373
Tolosana R, Vera-Rodriguez R, Fierrez J et al (2020) Deepfakes and
beyond: a survey of face manipulation and fake detection. Inf
Fusion 64:131–148. https://doi.org/10.1016/j.inffus.2020.06.014
Tolosana R, Romero-Tapiador S, Fierrez J, et al. (2021) Deepfakes
evolution: Analysis of facial regions and fake detection perfor-
mance. Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioin-
formatics) 12665 LNCS:442–456. https://doi.org/10.1007/978-3-
030-68821-9_38
Tran VN, Lee SH, Le HS et al (2021) High performance deepfake video
detection on cnn-based with attention target-specific regions and
manual distillation extraction. Appl Sci (Switzerland). https://doi.
org/10.3390/app11167678
Trinh L, Tsang M, Rambhatla S, et al. (2021) Interpretable and
trustworthy deepfake detection via dynamic prototypes. In: Pro-
ceedings - 2021 IEEE Winter Conference on Applications of
Computer Vision, WACV 2021, pp 1972–1982, https://doi.org/
10.1109/WACV48630.2021.00202
Tu Y, Liu Y, Li X (2021) Deepfake video detection by using convo-
lutional gated recurrent unit. In: ACM International Conference
Proceeding Series, pp 356–360, https://doi.org/10.1145/3457682.
3457736
Tulk Jesso S, Kennedy W, Wiese E (2020) Behavioral cues of human-
ness in complex environments: How people engage with human
and artificially intelligent agents in a multiplayer videogame. Fron-
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Deepfakes: evolution... 11317
tiers in Robotics and AI 7. https://doi.org/10.3389/frobt.2020.
531805
Tursman E, George M, Kamara S, et al. (2020) Towards untrusted social
video verification to combat deepfakes via face geometry consis-
tency.In: IEEE Computer Society Conference on Computer Vision
and Pattern Recognition Workshops, pp 2784–2793, https://doi.
org/10.1109/CVPRW50498.2020.00335
Valenzuela A, Segura C, Diego F, et al. (2021) Expression transfer using
flow-based generative models. In: IEEE Computer Society Confer-
ence on Computer Vision and Pattern Recognition Workshops, pp
1023–1031, https://doi.org/10.1109/CVPRW53098.2021.00113
Verdoliva L (2020) Media forensics and deepfakes: an overview. IEEE
J Sel Top Sign Proces 14(5):910–932. https://doi.org/10.1109/
JSTSP.2020.3002101
Vizoso A, Vaz-Álvarez M, López-García X (2021) Fighting deepfakes:
media and internet giants’ converging and diverging strategies
against hi-tech misinformation. Media Commun 9(1):291–300.
https://doi.org/10.17645/MAC.V9I1.3494
Wahl-Jorgensen K, Carlson M (2021) Conjecturing fearful futures: jour-
nalistic discourses on deepfakes. J Pract 15(6):803–820. https://
doi.org/10.1080/17512786.2021.1908838
Wang Y, Dantcheva A (2020) A video is worth more than 1000 lies.
comparing 3dcnn approaches for detecting deepfakes. In: Struc V.
GFF (ed) Proceedings - 2020 15th IEEE International Conference
on Automatic Face and Gesture Recognition, FG 2020, pp 515–
519, https://doi.org/10.1109/FG47880.2020.00089
Wang R, Juefei-Xu F, Huang Y, et al. (2020a) Deepsonar: Towards
effective and robust detection of ai-synthesized fake voices. In:
MM 2020 - Proceedings of the 28th ACM International Conference
on Multimedia, pp 1207–1216, https://doi.org/10.1145/3394171.
3413716
Wang R, Juefei-Xu F, Ma L, et al. (2020b) Fakespotter: A simple yet
robust baseline for spotting ai-synthesized fake faces. In: C. B (ed)
IJCAI International Joint Conference on Artificial Intelligence, pp
3444–3451
Wang X, Yao T, Ding S, et al. (2020c) Face manipulation detection via
auxiliary supervision. Lecture Notes in Computer Science (includ-
ing subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics) 12532 LNCS:313–324. https://doi.org/
10.1007/978-3-030-63830-6_27
Ward J (2019) 10 things judges should know about ai. Judicature
103(1):12–18
WesterlundM (2019) The emergence of deepfake technology: A review.
Technol Innov Manag Rev 9(11)
Whler L, Zembaty M (2021) Towards understanding perceptual difer-
ences between genuine and face-swapped videos. In: Conference
on Human Factors in Computing Systems - Proceedings, https://
doi.org/10.1145/3411764.3445627
Wu J, Feng K, Chang X, et al. (2020a) A forensic method for deepfake
image based on face recognition. In: ACM International Con-
ference Proceeding Series, pp 104–108, https://doi.org/10.1145/
3409501.3409544
Wu X, Xie Z, Gao Y, et al. (2020b) Sstnet: Detecting manipulated faces
through spatial, steganalysis and temporal features. In: ICASSP,
IEEE International Conference on Acoustics, Speech and Signal
Processing - Proceedings, pp 2952–2956, https://doi.org/10.1109/
ICASSP40776.2020.9053969
Xiang Z, Horvath J, Baireddy S, et al. (2021) Forensic analysis of video
files using metadata. In: IEEE Computer Society Conference on
Computer Vision and Pattern Recognition Workshops, pp 1042–
1051, https://doi.org/10.1109/CVPRW53098.2021.00115
Xie D, Chatterjee P, Liu Z, et al. (2020) Deepfake detection on pub-
licly available datasets using modified alexnet. In: 2020 IEEE
Symposium Series on Computational Intelligence, SSCI 2020, pp
1866–1871, https://doi.org/10.1109/SSCI47803.2020.9308428
Xu B, Liu J, Liang J et al (2021) Deepfake videos detection based
on texture features. Comput Mater Continua 68(1):1375–1388.
https://doi.org/10.32604/cmc.2021.016760
Xuan X, Peng B, Wang W, et al. (2019) On the generalization of
gan image forensics. Lecture Notes in Computer Science (includ-
ing subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics) 11818 LNCS:134–141. https://doi.org/
10.1007/978-3-030-31456-9_15
Xu Y, Jia G, Huang H, et al. (2021b) Visual-semantic transformer
for face forgery detection. In: 2021 IEEE International Joint
Conference on Biometrics, IJCB 2021, https://doi.org/10.1109/
IJCB52358.2021.9484407
Yang CZ, Ma J, Wang S et al (2021) Preventing deepfake attacks on
speaker authentication by dynamic lip movement analysis. IEEE
Trans Inf Forensics Secur 16:1841–1854. https://doi.org/10.1109/
TIFS.2020.3045937
Yang J, Li A, Xiao S et al (2021) Mtd-net: Learning to detect
deepfakes images by multi-scale texture difference. IEEE Trans
Inf Forensics Secur 16:4234–4245. https://doi.org/10.1109/TIFS.
2021.3102487
Yang J, Xiao S, Li A et al (2021) Detecting fake images by identifying
potential texture difference. Futur Gener Comput Syst 125:127–
135. https://doi.org/10.1016/j.future.2021.06.043
Yang C, Ding L, Chen Y, et al. (2021a) Defending against gan-based
deepfake attacks via transformation-aware adversarial faces. In:
Proceedings of the International Joint Conference on Neural Net-
works, https://doi.org/10.1109/IJCNN52387.2021.9533868
Yang X, Li Y, Lyu S (2019a) Exposing deep fakes using inconsistent
head poses. pp 8261–8265, https://doi.org/10.1109/ICASSP.2019.
8683164, conference of 44th IEEE International Conference on
Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Con-
ference Date: 12 May 2019 Through 17 May 2019; Conference
Code:149034
Yang X, Li Y, Lyu S (2019b) Exposing deep fakes using inconsistent
head poses. In: ICASSP, IEEE International Conference on Acous-
tics, Speech and Signal Processing - Proceedings, pp 8261–8265,
https://doi.org/10.1109/ICASSP.2019.8683164
Yang C, Lim SN (2020) One-shot domain adaptation for face genera-
tion. In: Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, pp 5920–5929, https://
doi.org/10.1109/CVPR42600.2020.00596
Yang T, Wu J, Liu L, et al. (2020) Vtd-net: Depth face forgery oriented
video tampering detection based on convolutional neural network.
In: Fu J. SJ (ed) Chinese Control Conference, CCC, pp 7247–7251,
https://doi.org/10.23919/CCC50068.2020.9188580
Yao T,Qu C, Liu Q, et al. (2021) Compound figure separation of biomed-
ical images with side loss. arXiv:2107.08650
Yavuzkilic S, Sengur A, Akhtar Z et al (2021) Spotting deepfakes
and face manipulations by fusing features from multi-stream cnns
models. Symmetry. https://doi.org/10.3390/sym13081352
Younus M, Hasan T (2020a) Abbreviated view of deepfake videos
detection techniques. In: Proceedings of the 6th International Engi-
neering Conference ”Sustainable Technology and Development”,
IEC 2020, pp 115–120, https://doi.org/10.1109/IEC49899.2020.
9122916
Younus M, Hasan T (2020b) Effective and fast deepfake detection
method based on haar wavelet transform. In: Proceedings of the
2020 International Conference on Computer Science and Software
Engineering, CSASE 2020, pp 186–190, https://doi.org/10.1109/
CSASE48920.2020.9142077
Yu M, Zhang J, Li S et al (2021) Deep forgery discriminator via image
degradation analysis. IET Image Proc 15(11):2478–2493. https://
doi.org/10.1049/ipr2.12234
Zendran M, Rusiecki A (2021) Swapping face images with generative
neural networks for deepfake technology - experimental study. In:
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

11318 R. Gil et al.
Procedia Computer Science, pp 834–843, https://doi.org/10.1016/
j.procs.2021.08.086
Zeng Y, Guo X, Yang Y, et al. (2020) Dfdm - a deepfakes detection
model based on steganography forensic network. Communica-
tions in Computer and Information Science 1253 CCIS:536–545.
https://doi.org/10.1007/978-981-15-8086-4_51
Zhang K, Liang Y, Zhang J et al (2019) No one can
escape: a general approach to detect tampered and generated
image. IEEE Access 7:129494–129503. https://doi.org/10.1109/
ACCESS.2019.2939812
Zhang W, Zhao C, Li Y (2020) A novel counterfeit feature extrac-
tion technique for exposing face-swap images based on deep
learning and error level analysis. Entropy. https://doi.org/10.3390/
e22020249
Zhang H, Lu ZM, Luo H et al (2021) Restore deepfakes video frames
via identifying individual motion styles. Electron Lett. https://doi.
org/10.1049/ell2.12015
Zhang Y, Gao F, Zhou Z, et al. (2021b) A survey on face forgery detec-
tion of deepfake. In: Jiang X. FH (ed) Proceedings of SPIE - The
International Society for Optical Engineering, https://doi.org/10.
1117/12.2600889
Zhang X, Karaman S, Chang SF (2019b) Detecting and simulating
artifacts in gan fake images. https://doi.org/10.1109/WIFS47025.
2019.9035107, conference of 2019 IEEE International Workshop
on Information Forensics and Security, WIFS 2019 ; Conference
Date: 9 December 2019 Through 12 December 2019; Conference
Code:158617
Zhang X, Karaman S, Chang SF (2019c) Detecting and simulating arti-
facts in gan fake images. In: 2019 IEEE International Workshop on
Information Forensics and Security, WIFS 2019, https://doi.org/
10.1109/WIFS47025.2019.9035107
Zhao B, Zhang S, Xu C et al (2021) Deep fake geography? when geospa-
tial data encounter artificial intelligence. Cartogr Geogr Inf Sci
48(4):338–352. https://doi.org/10.1080/15230406.2021.1910075
Zhao Z, Wang P, Lu W (2021) Multi-layer fusion neural network for
deepfake detection. Int J Digit Crim Forensics 13(4):26–39. https://
doi.org/10.4018/IJDCF.20210701.oa3
Zhao Y, Ge W, Li W, et al. (2020a) Capturing the persistence of facial
expression features for deepfake video detection. Lecture Notes
in Computer Science (including subseries Lecture Notes in Arti-
ficial Intelligence and Lecture Notes in Bioinformatics) 11999
LNCS:630–645. https://doi.org/10.1007/978-3-030-41579-2_37
Zhao Z, Wang P, Lu W (2020b) Detecting deepfake video by learning
two-level features with two-stream convolutional neural network.
In: ACM International Conference Proceeding Series, pp 291–297,
https://doi.org/10.1145/3404555.3404564
Zheng Q, Yang M, Yang J et al (2018) Improvement of generalization
ability of deep cnn via implicit regularization in two-stage training
process. IEEE Access 6:15,844-15,869. https://doi.org/10.1109/
ACCESS.2018.2810849
Zhu B, Fang H, Sui Y, et al. (2020a) Deepfakes for medical video
de-identification: Privacy protection and diagnostic information
preservation. In: AIES 2020 - Proceedings of the AAAI/ACM
Conference on AI, Ethics, and Society, pp 414–420, https://doi.
org/10.1145/3375627.3375849
Zhu H, Fu C, Wu Q, et al. (2020b) Aot: Appearance optimal transport
based identity swapping for forgery detection. In: Advances in
Neural Information Processing Systems
Zhu K, Wu B, Wang B (2020c) Deepfake detection with clustering-
based embedding regularization. In: Proceedings - 2020 IEEE
5th International Conference on Data Science in Cyberspace,
DSC 2020, pp 257–264, https://doi.org/10.1109/DSC50466.2020.
00046
Zi B, Chang M, Chen J, et al. (2020) Wilddeepfake: A challenging
real-world dataset for deepfake detection. In: MM 2020 - Proceed-
ings of the 28th ACM International Conference on Multimedia, pp
2382–2390, https://doi.org/10.1145/3394171.3413769
Zotov S, Dremliuga R, Borshevnikov A, et al. (2020) Deepfake
detection algorithms: A meta-analysis. In: ACM International
Conference Proceeding Series, pp 43–48, https://doi.org/10.1145/
3421515.3421532
Publisher’s Note Springer Nature remains neutral with regard to juris-
dictional claims in published maps and institutional affiliations.
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