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The Emergence of Deepfake Technology: A Review



Novel digital technologies make it increasingly difficult to distinguish between real and fake media. One of the most recent developments contributing to the problem is the emergence of deepfakes which are hyper-realistic videos that apply artificial intelligence (AI) to depict someone say and do things that never happened. Coupled with the reach and speed of social media, convincing deepfakes can quickly reach millions of people and have negative impacts on our society. While scholarly research on the topic is sparse, this study analyzes 84 publicly available online news articles to examine what deepfakes are and who produces them, what the benefits and threats of deepfake technology are, what examples of deepfakes there are, and how to combat deepfakes. The results suggest that while deepfakes are a significant threat to our society, political system and business, they can be combatted via legislation and regulation, corporate policies and voluntary action, education and training, as well as the development of technology for deepfake detection, content authentication, and deepfake prevention. The study provides a comprehensive review of deepfakes and provides cybersecurity and AI entrepreneurs with business opportunities in fighting against media forgeries and fake news.
The Emergence of Deepfake Technology:
A Review
Mika Westerlund
In recent years, fake news has become an issue that is a
threat to public discourse, human society, and
democracy (Borges et al., 2018; Qayyum et al., 2019).
Fake news refers to fictitious news style content that is
fabricated to deceive the public (Aldwairi & Alwahedi,
2018; Jang & Kim, 2018). False information spreads
quickly through social media, where it can impact
millions of users (Figueira & Oliveira, 2017). Presently,
one out of five Internet users get their news via YouTube,
second only to Facebook (Anderson, 2018). This rise in
popularity of video highlights the need for tools to
confirm media and news content authenticity, as novel
technologies allow convincing manipulation of video
(Anderson, 2018). Given the ease in obtaining and
spreading misinformation through social media
platforms, it is increasingly hard to know what to trust,
which results in harmful consequences for informed
decision making, among other things (Borges et al.,
2018; Britt et al., 2019). Indeed, today we live in what
some have called a “post-truth” era, which is
characterized by digital disinformation and information
warfare led by malevolent actors running false
information campaigns to manipulate public opinion
(Anderson, 2018; Qayyum et al., 2019; Zannettou et al.,
Recent technological advancements have made it easy to
create what are now called “deepfakes”, hyper-realistic
videos using face swaps that leave little trace of
manipulation (Chawla, 2019). Deepfakes are the product
of artificial intelligence (AI) applications that merge,
combine, replace, and superimpose images and video
clips to create fake videos that appear authentic (Maras
& Alexandrou, 2018). Deepfake technology can generate,
for example, a humorous, pornographic, or political
video of a person saying anything, without the consent
of the person whose image and voice is involved (Day,
2018; Fletcher, 2018). The game-changing factor of
deepfakes is the scope, scale, and sophistication of the
technology involved, as almost anyone with a computer
can fabricate fake videos that are practically
indistinguishable from authentic media (Fletcher, 2018).
While early examples of deepfakes focused on political
leaders, actresses, comedians, and entertainers having
their faces weaved into porn videos (Hasan & Salah,
2019), deepfakes in the future will likely be more and
more used for revenge porn, bullying, fake video
evidence in courts, political sabotage, terrorist
propaganda, blackmail, market manipulation, and fake
news (Maras & Alexandrou, 2019).
While spreading false information is easy, correcting the
record and combating deepfakes are harder (De
Novel digital technologies make it increasingly difficult to distinguish between real and fake media. One
of the most recent developments contributing to the problem is the emergence of deepfakes which are
hyper-realistic videos that apply artificial intelligence (AI) to depict someone say and do things that
never happened. Coupled with the reach and speed of social media, convincing deepfakes can quickly
reach millions of people and have negative impacts on our society. While scholarly research on the
topic is sparse, this study analyzes 84 publicly available online news articles to examine what deepfakes
are and who produces them, what the benefits and threats of deepfake technology are, what examples
of deepfakes there are, and how to combat deepfakes. The results suggest that while deepfakes are a
significant threat to our society, political system and business, they can be combatted via legislation
and regulation, corporate policies and voluntary action, education and training, as well as the
development of technology for deepfake detection, content authentication, and deepfake prevention.
The study provides a comprehensive review of deepfakes and provides cybersecurity and AI
entrepreneurs with business opportunities in fighting against media forgeries and fake news.
This is developing more rapidly than I thought. Soon, it’s going to
get to the point where there is no way that we can actually detect
[deepfakes] anymore, so we have to look at other types of solutions.
Hao Li
Deepfake Pioneer & Associate Professor
keersmaecker & Roets, 2017). In order to fight against
deepfakes, we need to understand deepfakes, the
reasons for their existence, and the technology behind
them. However, scholarly research has only recently
begun to address digital disinformation in social media
(Anderson, 2018). As deepfakes only surfaced on the
Internet in 2017, scholarly literature on the topic is
sparse. Hence, this study aims to discuss what
deepfakes are and who produces them, what the
benefits and threats of deepfake technology are, some
examples of current deepfakes, and how to combat
them. In so doing, the study analyzes a number of news
articles on deepfakes drawn from news media websites.
The study contributes to the nascent literatures of fake
news and deepfakes both by providing a comprehensive
review of deepfakes, as well as rooting the emerging
topic into an academic debate that also identifies
options for politicians, journalists, entrepreneurs, and
others to combat deepfakes.
The article is organized as follows. After the
introduction, the study explains data collection and
news article analysis. The study then puts forward four
sections that review deepfakes, what the potential
benefits of deepfake technology are, who the actors
involved in producing deepfakes are, and the threats of
deepfakes to our societies, political systems, and
businesses. Thereafter, two sections provide examples
of deepfakes and discuss four feasible mechanisms to
combat deepfakes. Finally, the study concludes with
implications, limitations, and suggestions for future
This study relies on the emerging scholarly literature
and publicly available news articles on deepfakes. A
total of 84 articles from 11 news companies’ websites
were collected in August 2019 for the purpose of
conducting empirical analysis on how the news media
has discussed deepfakes. All articles focused on
deepfakes, were written in English and were published
in 2018-2019. They were found through Google News
search, using keywords “deepfake”, “deep fake”, and the
corresponding plural forms. Once an article was found,
a similar search was performed using the news website’s
own search option to find more articles by that
particular media source. The focus of the selected news
media ranged from general daily news to concentration
on business or technology news. The dataset includes 2
to 16 news articles on deepfakes from each news
company. The articles were coded with a short identifier
for citing purposes, then analyzed via content analysis
with focus on what deepfakes are, who produces them,
what the benefits and threats of deepfake technology
are, some current examples of deepfakes, and how to
combat them. Table 1 in the appendix shows the news
articles, their authors, news companies, and publication
dates; the article titles are shortened due to space
A combination of "deep learning" and "fake", deepfakes
are hyper-realistic videos digitally manipulated to
depict people saying and doing things that never
actually happened (CNN03; FRB04). Deepfakes rely on
neural networks that analyze large sets of data samples
to learn to mimic a person's facial expressions,
mannerisms, voice, and inflections (CBS02; PCM10).
The process involves feeding footage of two people into
a deep learning algorithm to train it to swap faces
(PCM01). In other words, deepfakes use facial mapping
technology and AI that swaps the face of a person on a
video into the face of another person (FOX09; PCM03).
Deepfakes surfaced to publicity in 2017 when a Reddit
user posted videos showing celebrities in compromising
sexual situations (FRB01; FRB08; USAT03). Deepfakes
are difficult to detect, as they use real footage, can have
authentic-sounding audio, and are optimized to spread
on social media quickly (FRB05; WP01). Thus, many
viewers assume that the video they are looking at is
genuine (CNET01; CNN10).
Deepfakes target social media platforms, where
conspiracies, rumors, and misinformation spread easily,
as users tend to go with the crowd (CNET05; FOX06). At
the same time, an ongoing ‘infopocalypse’ pushes
people to think they cannot trust any information
unless it comes from their social networks, including
family members, close friends or relatives, and supports
the opinions they already hold (CNN06). In fact, many
people are open to anything that confirms their existing
views even if they suspect it may be fake (GRD09).
Cheap fakes, that is, low-quality videos with slightly
doctored real content, are already everywhere because
low-priced hardware such as efficient graphical
processing units are widely available (CBS01; CNN08).
Software for crafting high-quality, realistic deepfakes for
disinformation is increasingly available as open source
(FOX05; FT02; PCM04). This enables users with little
technical skills and without any artistic expertise to
near-perfectly edit videos, swap faces, alter expressions,
and synthesize speech (CNET08; GRD10).
As for technology, deepfakes are the product of
Generative Adversarial Networks (GANs), namely two
artificial neural networks working together to create
The Emergence of Deepfake Technology: A Review
Mika Westerlund
real-looking media (CNN03). These two networks called
‘the generator’ and ‘the discriminator’ are trained on
the same dataset of images, videos, or sounds (GRD03).
The first then tries to create new samples that are good
enough to trick the second network, which works to
determine whether the new media it sees is real
(FBR07). That way, they drive each other to improve
(PCM05). A GAN can look at thousands of photos of a
person, and produce a new portrait that approximates
those photos without being an exact copy of any one of
them (GRD07). In the near future, GANs will be trained
on less information and be able to swap heads, whole
bodies, and voices (GRD08; USAT01). Although
deepfakes usually require a large number of images to
create a realistic forgery, researchers have already
developed a technique to generate a fake video by
feeding it only one photo such as a selfie (CBS03;
Deepfake technology also has positive uses in many
industries, including movies, educational media and
digital communications, games and entertainment,
social media and healthcare, material science, and
various business fields, such as fashion and e-
commerce (FRB04).
The film industry can benefit from deepfake technology
in multiple ways. For example, it can help in making
digital voices for actors who lost theirs due to disease, or
for updating film footage instead of reshooting it
(FRB01; PCM10). Movie makers will be able to recreate
classic scenes in movies, create new movies starring
long-dead actors, make use of special effects and
advanced face editing in post-production, and improve
amateur videos to professional quality (FOX05; GRD07).
Deepfake technology also allows for automatic and
realistic voice dubbing for movies in any language
(PCM09; USAT04), thus allowing diverse audiences to
better enjoy films and educational media. A 2019 global
malaria awareness campaign featuring David Beckham
broke down language barriers through an educational
ad that used visual and voice-altering technology to
make him appear multilingual (USAT03). Similarly,
deepfake technology can break the language barrier on
video conference calls by translating speech and
simultaneously altering facial and mouth movements to
improve eye-contact and make everyone appear to be
speaking the same language (CNET05; FRB03; FT03).
The technology behind deepfakes enables multiplayer
games and virtual chat worlds with increased
telepresence (CNET07), natural-sounding and -looking
smart assistants (PCM09) and digital doubles of people.
This helps to develop better human relationships and
interaction online (CBS03; FRB02). Similarly, the
technology can have positive uses in the social and
medical fields. Deepfakes can help people deal with the
loss of loved ones by digitally bringing a deceased friend
“back to life”, and thereby potentially aiding a grieving
loved one to say goodbye to her (FOX05; PCM10).
Further, it can digitally recreate an amputee’s limb or
allow transgender people to better see themselves as a
preferred gender (USAT04). Deepfake technology can
even help people with Alzheimer's interact with a
younger face they may remember (FOX05). Scientists
are also exploring the use of GANs to detect
abnormalities in X-rays (CNET04) and their potential in
creating virtual chemical molecules to speed up
materials science and medical discoveries (GRD03).
Businesses are interested in the potential of brand-
applicable deepfake technology, as it can transform e-
commerce and advertising in significant ways (FRB02).
For example, brands can contract supermodels who are
not really supermodels, and show fashion outfits on a
variety of models with different skin tones, heights, and
weights (FRB07). Further, deepfakes allow for
superpersonal content that turns consumers
themselves into models; the technology enables virtual
fitting to preview how an outfit would look on them
before purchasing and can generate targeted fashion
ads that vary depending on time, weather, and viewer
(FRB02; FRB07). An obvious potential use is being able
to quickly try on clothes online; the technology not only
allows people to create digital clones of themselves and
have these personal avatars travel with them across e-
stores, but also to try on a bridal gown or suit in digital
form and then virtually experience a wedding venue
(FRB02). Also, AI can provide unique artificial voices
that differentiate companies and products to make
branding distinction easier (PCM10).
There are at least four major types of deepfake
producers: 1) communities of deepfake hobbyists, 2)
political players such as foreign governments, and
various activists, 3) other malevolent actors such as
fraudsters, and 4) legitimate actors, such as television
Individuals in deepfake hobby communities are difficult
to track down (FRB06). After the introduction of
celebrity porn deepfakes to Reddit by one user in late
2017, it only took a few months for a newly founded
deepfake hobbyist community to reach 90,000 members
The Emergence of Deepfake Technology: A Review
Mika Westerlund
It is highly probably that the journalism industry is
going to have to face a massive consumer trust issue
due to deepfakes (USAT01). Deepfakes pose a greater
threat than “traditional” fake news because they are
harder to spot and people are inclined to believe the
fake is real (CNN06). The technology allows the
production of seemingly legitimate news videos that
place the reputation of journalists and the media at risk
(USAT01). Also, winning the race to access video
footage shot by the witness of an incident can provide
competitive advantage to a news media outlet, while
danger rises if the offered footage is fake. During the
spike in tensions between India and Pakistan in 2019,
Reuters found 30 fake videos on the incident; mostly old
videos from other events posted with new captions
(DD02). Misattributed video footage such as a real
protest march or violent skirmish captioned to suggest
it happened somewhere else is a growing problem, and
will be augmented by the rise of deepfakes (WP01).
While looking for eyewitness videos about the mass
shooting in Christchurch, New Zealand, Reuters came
across a video which claimed to show the moment a
suspect was shot dead by police. However, they quickly
discovered it was from a different incident in the U.S.A.,
and the suspect in the Christchurch shooting was not
killed (DD02).
The intelligence community is concerned that
deepfakes will be used to threaten national security by
disseminating political propaganda and disrupting
election campaigns (CNET07; CNN10). U.S. intelligence
officials have repeatedly warned about the threat of
foreign meddling in American politics, especially in the
lead-up to elections (CNN02; CNET04). Putting words in
someone's mouth on a video that goes viral is a
powerful weapon in today’s disinformation wars, as
such altered videos can easily skew voter opinion
(USAT02; WP02). A foreign intelligence agency could
produce a deepfake a video of a politician using a racial
epithet or taking a bribe, a presidential candidate
confessing complicity in a crime, or warning another
country of an upcoming war, a government official in a
seemingly compromising situation, or admitting a
secret plan to carry out a conspiracy, or U.S. soldiers
committing war crimes such as killing civilians overseas
(CBS02; CNN06; FOX06). While such faked videos would
likely cause domestic unrest, riots, and disruptions in
elections, other nation states could even choose to act
out their foreign policies based on unreality, leading to
international conflicts (CBS03).
Deepfakes are likely to hamper digital literacy and
citizens’ trust toward authority-provided information,
as fake videos showing government officials saying
(CBS01; GRD08). Many hobbyists focus on porn-related
deepfakes (USAT01), while others place famous actors
in films in which they never appeared to produce comic
effects (GRD05). Overall, hobbyists tend to see AI-
crafted videos as a new form of online humor, and
contribution to the development of such technology as
solving an intellectual puzzle, rather than as a way to
trick or threaten people (CNN07; GRD05). Their
deepfakes are meant to be entertaining, funny, or
politically satirical, and can help with gaining followers
on social media (FOX01). Some hobbyists may be
looking for more concrete personal benefits, such as
raising awareness about the potential of deepfake
technology in order to get deepfake-related paid work,
for example, with music videos or television shows
(GRD02). Thus, hobbyists and legitimate actors such as
television companies may collaborate with each other.
While meme-like deepfakes by hobbyists can entertain
online users, more malicious actors are also involved.
Various political players, including political agitators,
hacktivists, terrorists, and foreign states can use
deepfakes in disinformation campaigns to manipulate
public opinion and undermine confidence in a given
country’s institutions (CBS01; CBS02). In these times of
hybrid warfare, deepfakes are weaponized
disinformation aimed at interfering with elections and
sowing civil unrest (CNET12). We may anticipate more
and more domestic and foreign state-funded Internet
“troll farms” that use AI to create and deliver political
fake videos tailored to social media users' specific biases
(CNN06). Deepfakes are also increasingly being
deployed by fraudsters for the purpose of conducting
market and stock manipulation, and various other
financial crimes (USAT03). Criminals have already used
AI-generated fake audios to impersonate an executive
on the phone asking for an urgent cash transfer
(CNN01; FT01). In the future, video calls will also be
able to be faked in real-time. Visual materials required
to produce impersonations of executives are often
available on the Internet. Deepfake technology can
make use of visual and audio impersonations of
executives from, for example, TED Talk videos available
on YouTube (WP01).
Deepfakes are a major threat to our society, political
system, and business because they 1) put pressure on
journalists struggling to filter real from fake news, 2)
threaten national security by disseminating propaganda
and interfering in elections, 3) hamper citizen trust
toward information by authorities, and, 4) raise
cybersecurity issues for people and organizations.
The Emergence of Deepfake Technology: A Review
Mika Westerlund
things that never happened make people doubt
authorities (CNET11; FOX10). Indeed, people nowadays
are increasingly affected by AI-generated spam, and by
fake news that builds on bigoted text, faked videos, and
a plethora of conspiracy theories (GRD06). Nonetheless,
the most damaging aspect of deepfakes may not be
disinformation per se, but rather how constant contact
with misinformation leads people to feel that much
information, including video, simply cannot be trusted,
thereby resulting in a phenomenon termed as
"information apocalypse" or “reality apathy” (CNN01;
GRD07). Further, people may even dismiss genuine
footage as fake (CBS02), simply because they have
become entrenched in the notion that anything they do
not want to believe must be fake (CNET05). In other
words, the greatest threat is not that people will be
deceived, but that they will come to regard everything
as deception (GRD07).
Cybersecurity issues constitute another threat imposed
by deepfakes. The corporate world has already
expressed interest in protecting themselves against viral
frauds, as deepfakes could be used for market and stock
manipulation, for example, by showing a chief executive
saying racist or misogynistic slurs, announcing a fake
merger, making false statements of financial losses or
bankruptcy, or portraying them as if committing a
crime (CNN02; FRB04; WP01). Further, deepfaked porn
or product announcements could be used for brand
sabotage, blackmail, or to embarrass management
(FRB06; PCM03). In addition, deepfake technology
enables real-time digital impersonation of an executive,
for example, to ask an employee to perform an urgent
cash transfer or provide confidential information
(CNN01; FT01; PCM03). Further, deepfake technology
can create a fraudulent identity and, in live-stream
videos, convert an adult face into a child’s or younger
person’s face, raising concerns about the use of the
technology by child predators (FOX06). Lastly,
deepfakes can contribute to the spread of malicious
scripts. Recently, researchers found that a website
devoted to deepfakes used its visitors’ computers to
mine cryptocurrencies. Deepfake hobbyists may in this
way become targets of ‘cryptojacking’ because they are
likely to have powerful computers (CNET16).
Most deepfakes today on social platforms like YouTube
or Facebook can be seen as harmless fun or artistic
works using dead or alive public figures. But there are
also examples from the dark side of deepfakes, namely
celebrity and revenge porn, as well as attempts at
political and non-political influencing.
Many deepfakes focus on celebrities, politicians, and
corporate leaders because the internet is packed with
source photos and videos of them from which to build
the large image stockpiles required to train an AI
deepfake system (CNET08; PCM03). The majority of
such deepfakes are goofs, pranks, and funny memes
with comedic or satirical effect (CNET07; DD01). A
deepfake might show, for example, Nicolas Cage acting
in movies in which he has never starred in, such as
Indiana Jones or Terminator 2 (CNET05; PCM10). Some
intriguing examples of deepfakes include a video that
replaces Alden Ehrenreich with young Harrison Ford in
clips taken from Solo: A Star Wars Story, and a video of
actor Bill Hader appearing on Late Night with David
Letterman. While Hader talks about Tom Cruise, his
face morphs into Cruise's (CNET01; FRB06). Some
deepfakes show dead celebrities such as the band
Queen’s ex-vocalist Freddie Mercury’s face imposed on
that of actor Rami Malek’s, along with the Russian
mystic Grigori Rasputin singing Beyonce's powerful
ballad "Halo" (FOX02). An art museum in the U.S. has
used the technology to bring Salvador Dali back to life
to greet visitors (DD01), and another AI system makes
anyone dance like a prima ballerina by imposing a real
dancer’s moves onto a target person's body, thereby
generating a video that shows the target as a
professional dancer (CNET14; PCM05).
Examples of harmful deepfakes, however, are also
popping up increasingly (FOX04). Deepfake technology
enables celebrity and revenge porn, that is, involuntary
pornography using images of celebrities and non-
celebrities, which are shared on social networks without
their consent (CNET07; CNET15). Thus, celebrities such
as Scarlett Johansson have been featured on deepfaked
adult movies, in which their faces have been
superimposed over porn stars' faces (CNET08; PCM03).
In the political scene, a 2018 deepfake created by
Hollywood filmmaker Jordan Peele featured former US
President Obama discussing the dangers of fake news
and mocking the current president Trump (CBS01;
CNN06). In 2019, an altered video of American
politician Nancy Pelosi went viral and had massive
outreach; the video was slowed down to make her
sound intoxicated (CNET01; FRB06). In a 2018 deepfake
video, Donald Trump offered advice to the people of
Belgium about climate change. The video was created
by a Belgian political party “sp.a” in order to attract
people to sign an online petition calling on the Belgian
government to take more urgent climate action. The
video provoked outrage about the American president
meddling in a foreign country with Belgium’s climate
policy (GRD07). In 2019, the U.S. Democratic Party
deepfaked its own chairman Tom Perez to highlight the
The Emergence of Deepfake Technology: A Review
Mika Westerlund
potential threat of deepfakes to the 2020 election
While these are examples of limited political
influencing, other deepfakes may have more lasting
impact. In Central Africa in 2018, a deepfake of Gabon’s
long-unseen president Ali Bongo, who was believed in
poor health or dead, was cited as the trigger for an
unsuccessful coup by the Gabonese military. And in
Malaysia, a viral clip deepfake of a man’s confession to
having sex with a local cabinet minister caused political
controversy (WP01). Also non-political individuals have
been used for creating deepfakes. In June 2019, a high-
quality deepfake by two British artists featuring
Facebook CEO Mark Zuckerberg racked up millions of
views (CBS01). The video falsely portrays Zuckerberg
giving respect to Spectre, a fictional evil organization
from the James Bond series that teaches him how to
take total control of billions of peoples’ confidential
data, and thus own their future (CNN04; FOX03;
FRB05). Using news footage, deepfake technology, and
a voice actor, the video was meant to show how
technology can be used to manipulate data (CNN05).
The reviewed news articles suggest that there are four
ways to combat deepfakes: 1) legislation and regulation,
2) corporate policies and voluntary action, 3) education
and training, and 4) anti-deepfake technology that
includes deepfake detection, content authentication,
and deepfake prevention.
Legislation and regulation are both obvious means
against deepfakes. At present, deepfakes are not
specifically addressed by civil or criminal laws, although
legal experts have suggested adapting current laws to
cover libel, defamation, identity fraud, or impersonating
a government official using deepfakes (FT02; WP01).
Virginia state law against revenge porn recently made
distributing "falsely created" images and videos a
misdemeanor, and thus expanded the law to include
deepfakes (CNET03). That said, the increasing
sophistication of AI technologies calls for new types of
laws and regulatory frameworks (GRD03). For example,
deepfakes raise concerns about privacy and copyright,
as the visual depictions of people on deepfake videos
are not exact copies of any existing material, but rather
new representations generated by AI (CNET03; GRD07).
Thus, regulators must navigate a difficult legal
landscape around free-speech and ownership laws to
properly regulate the use of deepfake technology
On the other hand, an appropriate legal solution to the
proliferation of harmful deepfakes would not be a
complete ban on the technology, which would be
unethical (USAT04). While new laws can be introduced
to prevent deepfakes, they also need mechanisms of
enforcement (FRB09). Today’s social media firms enjoy
broad immunity for the content that users post on their
platforms (WP02). One legislative option could be to
walk back social media firms' legal immunity from the
content their users post, thus making not only users but
also the platforms more responsible for posted material
(CNET09). Nonetheless, legislation has had little effect
on malevolent actors such as foreign states and
terrorists, that may run massive disinformation
campaigns against other states on social media
Corporate policies and voluntary action may provide
more effective tools against deepfakes. For example,
politicians can commit not to use illicit digital campaign
tactics or spread disinformation such as deepfakes in
their election campaigns (WP04). Social media
companies need to enforce ethics and turn away from
the fact that divisive content getting pushed to the top
of the feed is financially a win because it maximizes
engagement time for advertisements (GRD01). While
few social media firms have policies yet about
deepfakes, they should collaborate to prevent their
platforms from being weaponized for disinformation,
and thus proactively enforce transparent, shared
policies to block and remove deepfakes (CNET10;
FOX06; GRD04). Presently, many companies do not
remove disputed content, rather they downrank it to
make it more difficult to find, by being less prominent
in users’ news feeds (CNN04; FOX02; FOX03). On the
other hand, the increase in hate speech, fake news, and
disinformation polluting digital platforms has led some
firms to take more action, such as suspending user
accounts and investing in quicker detection technology
(CNET03; CNN05; CNN06). Reddit and Pornhub have
banned deepfake porn and other non-consensual
pornography, and act upon users’ flagging of such
material (CNET15; FRB10; PCM07). Facebook cuts off
any content identified as false or misleading by third-
party fact-checkers from running ads and making
money; the company works with over 50 fact-checking
organizations, academics, experts, and policymakers to
find new solutions (CNET06; CNET09; CNET11).
Instagram’s algorithms will not recommend people
view content that is marked as “false” by fact checkers
(CNN04). Among news media companies, Wall Street
Journal and Reuters have formed corporate teams to
help and train their reporters to identify fake content,
The Emergence of Deepfake Technology: A Review
Mika Westerlund
and to adopt detection techniques and tools such as
cross-referencing location on Google maps and reverse
image searching (DD01; DD02; CNN01).
Education and training are crucial for combatting
deepfakes. Despite considerable news coverage and
concerns presented by authorities, the threat of deep
fakes has not yet been reckoned with by the public
(FRB08). In general, there is a need to raise public
awareness about AI’s potential for misuse (FOX01).
Whereas deepfakes provide cyber criminals new tools
for social engineering, companies and organisations
need to be on high alert and to establish cyber resilience
plans (FT01). Governments, regulators, and individuals
need to comprehend that video, contrary to
appearances, may not provide an accurate
representation of what happened, and know which
perceptual cues can help to identify deepfakes (USAT01;
WP01). It is recommended that critical thinking and
digital literacy be taught in schools as these traits
contribute to children’s ability to spot fake news and
interact more respectfully with each other online.
These skills likewise should also be promoted among
the older, less technology-savvy population (GRD02;
FOX06). The reason for this is that people need to be
able to critically assess the authenticity and social
context of a video they may wish to consume, as well as
the trustworthiness of its source (that is, who shared the
video and what does that say), in order to understand
the video’s real intent. It is also important to remember
that quality is not an indicator of a video’s authenticity
(FOX04; FRB01). Also, people need to understand that
as the technology develops, fewer photographs of real
faces will be required to create deepfakes and that
nobody is immune (FRB06). Anyone who posts a single
selfie or a video capturing 30 frames per second on a
social networking site is at risk of being deepfaked
(USAT03). While the best method is keeping photos and
videos off the internet, having obstructions such as
waving hand in front of a face in a photo or on a video
can provide some protection (CNET08). Companies,
governments, and authorities using facial recognition
technology and storing vast amounts of facial data for
security and verification purposes, need to address the
threat of identity theft if such data were to be leaked
Anti-deepfake technology provides perhaps the most
varied set of tools to 1) detect deepfakes, 2) authenticate
content, and 3) prevent content from being used to
produce deepfakes. Overall, the problems of technology
to authenticate content and identify fakes is doing it at
scale, and the fact that there are far more available
research resources and people working on developing
technology to create deepfakes than on technology to
detect them (CBS02; WP02). For instance, users upload
500 hours of content per minute on YouTube. Twitter
struggles with 8 million accounts a week that attempt to
spread content through manipulative tactics (PCM02;
WP01). This creates massive challenges for technologies
to go through all of the posted material in a short time.
Further, deepfake developers tend to use results from
published deepfake research to improve their
technology and get around new detection systems
(CNN06). For example, researchers found that early
deepfake methods failed to mimic the rate at which a
person blinks; whereas recent programs have fixed the
lack of blinking or unnatural blinking after the findings
were published (CNN03; GRD05). While funding for
deepfake detection development mainly comes from
national security agencies such as The Defense
Advanced Research Projects Agency (DARPA), there are
significant business opportunities for private
cybersecurity companies to produce solutions for
deepfake detection, build trusted platforms, weed out
illicit bots, and fight against fraud and digital pollution
(CBS03; FT01; FT02). However, the development of anti-
deepfake technology alone is not enough. Organizations
must also adopt these technologies; for example, the
government in any given country can be modernized to
face and help protect its citizens against deepfakes
Media forensic experts have suggested subtle indicators
to detect deepfakes, including a range of imperfections
such as face wobble, shimmer and distortion; waviness
in a person’s movements; inconsistencies with speech
and mouth movements; abnormal movements of fixed
objects such as a microphone stand; inconsistencies in
lighting, reflections and shadows; blurred edges; angles
and blurring of facial features; lack of breathing;
unnatural eye direction; missing facial features such as
a known mole on a cheek; softness and weight of
clothing and hair; overly smooth skin; missing hair and
teeth details; misalignment in face symmetry;
inconsistencies in pixel levels; and strange behavior of
an individual doing something implausible (CNET08;
CNET14; CNN09; GRD05; USAT03; USAT04; WP01).
While it is getting more and more difficult for people to
distinguish between a real video and a fake, AI can be
instrumental in detecting deepfakes (CBS02; FRB01).
For example, AI algorithms can analyze Photo Response
Non-Uniformity (PRNU) patterns in footage, that is,
imperfections unique to the light sensor of specific
camera models, or biometric data such as blood flow
indicated by subtle changes that occur on a person’s
face in a video (CNN06; GRD07; USAT01). New fake-
The Emergence of Deepfake Technology: A Review
Mika Westerlund
detection algorithms are based on mammalian auditory
systems, for example, the ways mice detect
inconsistencies and subtle mistakes in audio, which are
often ignored by humans (CNET02). AI can either look
at videos on a frame-by-frame basis to track signs of
forgery, or review the entire video at once to examine
soft biometric signatures, including inconsistencies in
the authenticated relationships between head
movements, speech patterns, and facial expressions
such as smiling, to determine if the video has been
manipulated (CNN03; FOX07). The latter method can
be tailored for individuals, such as high-profile
politicians who are likely to be deepfaked (PCM01).
The problem with deepfakes is not only about proving
something is false, but also about proving that an object
is authentic (FT02). Authentication of video is especially
important to news media companies who have to
determine authenticity of a video spreading in a
trustless environment, in which details of the video’s
creator, origin, and distribution may be hard to trace
(WP01). Proposed solutions to authenticate content
range from digital watermarks to digital forensic
techniques (FOX06; GRD01). It would be ideal to create
a “truth layer”, an automated system across the internet
to provide a fake vs. authentic measure of videos; that
way, every video posted to a social media site would go
through an authentication process (CBS03; USAT04).
For example, software embedded in smartphone
cameras can create a digital fingerprint at the moment
of a film’s recording. Upon footage playback, its
watermark can be compared with the original
fingerprint to check for a match, and provide the viewer
with a score that indicates the likelihood of tampering
(GRD05). Indeed, digital watermarking such as hashing
can provide a video file with a short string of numbers
that is lost if the video is manipulated (FOX04; FRB04).
It can also provide an authenticated alibi for public
figures, given that they constantly record where they are
and what they are doing (GRD03). Support for video
authenticity is also provided by mapping its
provenance, that is, whether the video came from a
reputable source originally, and how it has since
travelled online (FT01). Blockchain technology can help
in verifying the origins and distribution of videos by
creating and storing digital signatures in a ledger that is
almost impossible to manipulate (CNN06). Social media
platforms, news media companies and other online
actors should then promote the videos that are verified
as authentic over non-verified videos (USAT01).
Nonetheless, there will always be people who choose
not to believe a verification tool, and rather still have a
desire to consume and endorse fake media (USAT01).
Finally, technology can prevent the creation of
deepfakes by inserting “noise” into photos or videos.
This noise is imperceptible to the human eye, but
prevents the visual material from being used in
automated deepfake software (USAT04). One could also
wear specifically designed 3D-printed glasses to evade
facial recognition by tricking deepfake software into
misclassifying the wearer. This technology could help
likely targets such as politicians, celebrities and
executives to prevent deepfakes being made of them
(FT01). Also, researchers who are developing GAN
technologies can design and put proper safeguards in
place so that their technologies become more difficult
to misuse for disinformation purposes (FOX06). Similar
to the cybersecurity domain in general, the first step
towards a solution is understanding the problem and its
ability to affect us. Only then does it become possible to
develop and implement technical solutions that can
solve the challenges (FRB04). That said, none of the
technological solutions can entirely remove the risk of
deepfakes, and technological solutionism (that every
problem has a technological solution) may even
disorientate the discussion away from more existential
questions of why deepfakes exist and what other threats
AI can impose (GRD01; GRD03; GRD04). The most
efficient ways to combat deepfakes from spreading
therefore involve a mixture of legal, educational, and
sociotechnical advances (USAT01).
This study reviewed and analyzed 84 recent public news
articles on deepfakes in order to enable a better
understanding of what deepfakes are and who produces
them, the benefits and threats of deepfake technology,
examples of current deepfakes, and how to combat
them. In so doing, the study found that deepfakes are
hyper-realistic videos digitally manipulated to depict
people saying and doing things that never happened.
Deepfakes are created using AI, that is, Generative
Adversarial Networks (GANs) that pit discriminative and
generative algorithms against one another to fine-tune
performance with every repetition, and thereby
produce a convincing fake (Fletcher, 2018; Spivak,
2019). These fakes of real people are often highly viral
and tend to spread quickly through social media
platforms, thus making them an efficient tool for
The findings of this study offer several contributions to
the emerging body of scholarly literature on deepfakes
(see Anderson, 2018; Qayyum et al., 2019; Zannettou et
al., 2019). Previous research (Fletcher, 2018) argues that
The Emergence of Deepfake Technology: A Review
Mika Westerlund
deepfakes benefit from, 1) a citizenry increasingly
reliant upon commercial media platforms to absorb,
process, and communicate information, 2) a heated
political context where false narratives are easily spread
and easily believed online, and 3) the appearance of
powerful AI algorithms capable of manufacturing
seemingly real videos. Our findings support these
arguments by specifying that such commercial
platforms consist of both news media platforms and a
range of social media platforms, and that deepfakes are
not only fed by a heated political context, but also the
current social context due to the so-called information
apocalypse, which makes people cease trusting
information that does not come from their personal
social networks and is inconsistent with their prior
beliefs, a phenomenon addressed in previous literature
(Britt et al., 2019; Hamborg et al., 2018; Zannettou et al.,
2019). The increase in fake news business models that
generate web traffic to fake news pages to earn profit
through advertising, which has been discussed in
previous research (e.g., Figueira & Oliveira, 2017), did
not come up in the present study. A likely reason is that
the study analyzed news articles from journalists who
wish to avoid being associated with actors in the field of
journalism that rely on unethical methods such as
clickbaiting (cf. Aldwairi & Alwahedi, 2018).
Zannettou et al. (2019) list a number of actors
associated with deepfakes, ranging from governments,
political activists, criminals, and malevolent individuals
creating fake content to paid and unpaid trolls,
conspiracy theorists, useful idiots, and automated bots
disseminating it through social media platforms.
According to Zannettou et al. (2019), the motivation
behind these actors’ actions may include malicious
intent to hurt others in various ways, manipulate public
opinion with respect to specific topics, sow confusion or
discord to the public, monetary profit, passion about a
specific idea or organization or, as MacKenzie and Bhatt
(2018) note, plain fun and amusement. Our findings
highlight that there are also individuals and
organizations such as television companies that
generate and support deepfakes in order to develop and
apply deepfake technology for legit use such as paid
work for music videos. These are considered as early
examples of the benefits anticipated from applying
In regard to legitimate uses for deep learning
technology, previous research has addressed movie
studios, personalized advertisement companies, and
media broadcasters as potential benefiters. For
example, Netflix could enable watchers to pick on-
screen actors before hitting play or even enable
watchers themselves to star in the movie (Chawle,
2019). The present study identified a number of
additional areas for legitimate uses of the technology,
including educational media and digital
communications, games and entertainment, social and
healthcare, material science, and various business fields
such as fashion and personalized e-commerce.
According to our study, deepfakes are a major threat to
society, the political system and businesses because
they put pressure on journalists struggling to filter real
from fake news, threaten national security by
disseminating propaganda that interferes in elections,
hamper citizen trust toward information by authorities,
and raise cybersecurity issues for people and
organizations. In this vein, the study largely supports
the findings from previous research (Aldwairi &
Alwahedi, 2018; Bates, 2018; Chawla, 2019; Hamborg et
al., 2018; Lin, 2019; Wagner & Blewer, 2019) and, at the
same time, details these threats through examples of
existing and potential uses of deepfakes.
On the other hand, there are at least four known ways to
combat deepfakes, namely 1) legislation and regulation,
2) corporate policies and voluntary action, 3) education
and training, and 4) anti-deepfake technology. While
legislative action can be taken against some deepfake
producers, it is not effective against foreign states.
Rather, corporate policies and voluntary action such as
deepfake-addressing content moderation policies, and
quick removal of user-flagged content on social media
platforms, as well as education and training that aims at
improving digital media literacy, better online behavior
and critical thinking, which create cognitive and
concrete safeguards toward digital content
consumption and misuse, are likely to be more efficient.
Government authorities, companies, educators, and
journalists need to increase citizens’ awareness of the
threats posed by AI to media trust, and prohibit
fraudulent usage of such technologies for commercial,
political, or anti-social purposes. In this vein, our results
support and complement those presented by previous
studies (Anderson, 2018; Atodiresei et al., 2018; Britt et
al., 2019; Cybenko & Cybenko, 2018; Figueira & Oliveira,
2017; Floridi, 2018; Spivak, 2019).
Technological solutions, including automated tools for
deepfake detection, content authentication, and
deepfake prevention constitute a dynamic field of
security methods. Consequently, there are numerous
business opportunities for technology entrepreneurs,
especially in the areas of cybersecurity and AI. The
study highlights that deepfake technology is progressing
at an increasing pace. It is quickly becoming impossible
The Emergence of Deepfake Technology: A Review
Mika Westerlund
Aldwairi, M., & Alwahedi, A. 2018. Detecting Fake News
in Social Media Networks. Procedia Computer
Science, 141: 215–222.
Anderson, K. E. 2018. Getting acquainted with social
networks and apps: combating fake news on social
media. Library HiTech News, 35(3): 1–6.
Anwar, S., Milanova, M., Anwer, M., & Banihirwe, A.
2019. Perceptual Judgments to Detect Computer
Generated Forged Faces in Social Media. In F.
Schwenker, & S. Scherer (Eds.), Multimodal Pattern
Recognition of Social Signals in Human-Computer-
Interaction. MPRSS 2018. Lecture Notes in Computer
Science, vol. 11377. Springer, Cham.
Atanasova, P., Nakov, P., Màrquez, L., Barrón-Cedeño,
A., Karadzhov, G., Mihaylova, T., Mohtarami, M., &
Glass, J. 2019. Automatic Fact-Checking Using
Context and Discourse Information. Journal of Data
and Information Quality, 11(3): Article 12.
Atodiresei, C.-S., Tănăselea, A., & Iftene, A. 2018.
Identifying Fake News and Fake Users on Twitter.
Procedia Computer Science, 126: 451–461.
Bates, M. E. 2018. Say What? 'Deepfakes' Are Deeply
Concerning. Online Searcher, 42(4): 64.
Borges, L., Martins, B., & Calado, P. 2019. Combining
Similarity Features and Deep Representation
Learning for Stance Detection in the Context of
Checking Fake News. Journal of Data and
Information Quality, 11(3): Article No. 14.
Britt, M. A., Rouet, J.-F., Blaum, D., & Millis, K. 2019. A
Reasoned Approach to Dealing With Fake News.
Policy Insights from the Behavioral and Brain
Sciences, 6(1): 94–101.
Chawla, R. 2019. Deepfakes: How a pervert shook the
world. International Journal of Advance Research and
Development, 4(6): 4–8.
Cybenko, A. K., & Cybenko, G. 2018. AI and Fake News.
IEEE Intelligent Systems, 33(5): 3–7.
Day, C. 2019. The Future of Misinformation. Computing
in Science & Engineering, 21(1): 108–108.
De keersmaecker, J., & Roets, A. 2017. ‘Fake news’:
Incorrect, but hard to correct. The role of cognitive
ability on the impact of false information on social
impressions. Intelligence, 65: 107–110.
Figueira, A., & Oliveira, L. 2017. The current state of fake
news: challenges and opportunities. Procedia
Computer Science, 121: 817–825.
for human beings to distinguish between real and fake
videos. Hence, our results list numerous cues for
detecting deepfakes, and suggest harnessing AI in order
to detect AI-generated fakes as an efficient combat
strategy. At the same time, the study reckons that there
are growing needs for online source verification and
content authentication, and that ubiquitous truth layers
based on digital watermarks should be used. Further,
another emerging technology, namely blockhain can be
of help. Blockchain technology is not only highly
resistant to forgeries and can store data in an accruable,
safe, transparent, and traceable way, but it can also
track and certify the origins and history of the data
(Floridi, 2018). Again, these results are in line with
previous research (Anwar et al., 2019; Atanasova et al.,
2019; Bates, 2018; Chawla, 2019; Floridi, 2018; Hasan &
Salah, 2019; Qayyum et al., 2018; Spivak, 2019). In the
spirit of a review study, our results contribute to the
emerging field of deepfakes by pulling together
dispersed findings from the sparse academic research
on fake news and deepfakes, and by fine-tuning those
findings with examples and discussion on deepfakes
taking place in public news articles.
All said, there are certainly limitations in the study.
First, although the empirical research covered 84 online
news articles on deepfakes, there are many more
articles available and, given the speed of development
of this technology, those articles could also provide
additional information on deepfakes and suggest more
methods to fight them. Second, our empirical material
was collected from public sources, namely online news
media sites. Using other types of data, such as
deepfake-focused online community discussions and
interviews with GAN developers and deepfake artists,
some of whom are known to the public due to their
work not only as deepfake technology developers but
also as anti-deepfake technology developers, could give
additional insight on strategies to combat deepfakes.
Also, commentary sections in some of the analyzed
news articles had extensive amount of opinions and
ideas from readers; analysis of those comments might
give additional insights on how deepfakes are perceived
by a larger audience, and thus what education-oriented
combat methods should emphasize. These limitations
provide ample opportunities for future research on
The Emergence of Deepfake Technology: A Review
Mika Westerlund
Citation: Westerlund, M. 2019. The Emergence of Deepfake
Technology: A Review. Technology Innovation
Management Review, 9(11): 39-52.
Keywords: Deepfake, fake news, artificial intelligence, deep
learning, cybersecurity.
Fletcher, J. 2018. Deepfakes, Artificial Intelligence, and
Some Kind of Dystopia: The New Faces of Online
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455–471. Project MUSE,
Floridi, L. 2018. Artificial Intelligence, Deepfakes and a
Future of Ectypes. Philosophy & Technology, 31(3):
Hamborg, F., Donnay, K., & Gipp, B. 2018. Automated
identification of media bias in news articles: an
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Journal on Digital Libraries.
Hasan, H. R., & Salah, K. 2019. Combating Deepfake
Videos Using Blockchain and Smart Contracts. IEEE
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Jang, S. M., & Kim, J. K. 2018. Third person effects of fake
news: Fake news regulation and media literacy
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Lin, H. 2019. The existential threat from cyber-enabled
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75(4): 187–196.
MacKenzie, A., & Bhatt, I. 2018. Lies, Bullshit and Fake
News: Some Epistemological Concerns. Postdigital
Science and Education.
Maras, M. H., & Alexandrou, A. 2019. Determining
authenticity of video evidence in the age of artificial
intelligence and in the wake of Deepfake videos.
International Journal of Evidence & Proof, 23(3):
Qayyum, A., Qadir, J., Janjua, M. U. & Sher, F. 2019.
Using Blockchain to Rein in the New Post-Truth
World and Check the Spread of Fake News. IT
Professional, 21(4): 16–24.
Spivak, R. 2019. “Deepfakes”: The newest way to
commit one of the oldest crimes. The Georgetown
Law Technology Review, 3(2): 339–400.
Wagner, T.L., & Blewer, A. 2019. “The Word Real Is No
Longer Real”: Deepfakes, Gender, and the Challenges
of AI-Altered Video. Open Information Science, 3(1):
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis,
N. 2019. The Web of False Information: Rumors, Fake
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Mika Westerlund, DSc (Econ), is an Associate
Professor at Carleton University in Ottawa, Canada.
He previously held positions as a Postdoctoral
Scholar in the Haas School of Business at the
University of California Berkeley and in the School of
Economics at Aalto University in Helsinki, Finland.
Mika earned his doctoral degree in Marketing from
the Helsinki School of Economics in Finland. His
research interests include open and user innovation,
the Internet of Things, business strategy, and
management models in high-tech and service-
intensive industries.
The Emergence of Deepfake Technology: A Review
Mika Westerlund
: Studied news articles on deepfakes
The Emergence of Deepfake Technology: A Review
Mika Westerlund
The Emergence of Deepfake Technology: A Review
Mika Westerlund
The Emergence of Deepfake Technology: A Review
Mika Westerlund
... This is a complex issue because data made available by DE come from multiple sources, including data from non-authoritative sources (i.e. citizens), and we have already seen those issues of quality of the various data sources (Westerlund 2019). ...
... Now that the technology offers the possibility to create digital fakes quite easily, next-generation AI is threatening to take internet fakery to a dangerous new level. 'Deepfake' technology uses sophisticated AI to create video and audio that impersonates real people (Westerlund 2019). The technology is already in use, and if left unchecked, it could lead us to start doubting everything we watch and hear online. ...
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The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE. 'What is DE today?', 'What could DE be in the future?', and 'What is needed to make DE a reality?'. To answer these questions it is necessary to examine DE considering all the technological, scientific, social, and economic aspects, but also bearing in mind the principles that inspired its formulation. By understanding the lessons learned from the past, it becomes possible to identify the remaining scientific and technological challenges, and the actions needed to achieve the ultimate goal of a 'Digital Earth for all'. This article reviews the evolution of the DE vision and its multiple definitions, illustrates what has been achieved so far, explains the impact of digital transformation, illustrates the new vision, and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.
... Popularized through the notion of deepfakes [74], these deep-learning models are trained to replace any face in an image or video by another one (user-provided or AIgenerated), while trying to preserve the overall saliency or specific facial attributes, such as perceived gender, expression, or hair color. While recent solutions can generate convincing results, they are not suitable for the targeted use cases as they lack formal privacy and utility guarantees for the resulting images. ...
With the increasing ubiquity of cameras and smart sensors, humanity is generating data at an exponential rate. Access to this trove of information, often covering yet-underrepresented use-cases (e.g., AI in medical settings) could fuel a new generation of deep-learning tools. However, eager data scientists should first provide satisfying guarantees w.r.t. the privacy of individuals present in these untapped datasets. This is especially important for images or videos depicting faces, as their biometric information is the target of most identification methods. While a variety of solutions have been proposed to de-identify such images, they often corrupt other non-identifying facial attributes that would be relevant for downstream tasks. In this paper, we propose Disguise, a novel algorithm to seamlessly de-identify facial images while ensuring the usability of the altered data. Unlike prior arts, we ground our solution in both differential privacy and ensemble-learning research domains. Our method extracts and swaps depicted identities with fake ones, synthesized via variational mechanisms to maximize obfuscation and non-invertibility; while leveraging the supervision from a mixture-of-experts to disentangle and preserve other utility attributes. We extensively evaluate our method on multiple datasets, demonstrating higher de-identification rate and superior consistency than prior art w.r.t. various downstream tasks.
... counterfeit::pseudocontent This tactic creates deceptively realistic fake content by manual or automated methods [109], [129]. Our participants observe a large variance in sophistication employed to create fake content: from simple-yet-effective click baits that attract users to follow links to articles containing misinformation (n = 2), to cheap fakes (n = 2) generated with unsophisticated technology such as reusing stock images or existing profile pictures, to the use of deep fakes (n = 3), in which a person in an existing image or video is replaced with someone else's likeness to create hyper-realistic content using deep learning models [117]. Highlighting the deceptive capabilities of AI-generated content, Noel (P14) commented that "one out of three deepfakes is not properly identified." ...
... In addition, the news dataset used to train the model only consists of text. Fake news can also be spread through other types of content, such as audio clips, images, and videos [74]. Currently, there are no available COVID-19 fake news datasets that include these types of content. ...
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The spread of fake news related to COVID-19 is an infodemic that leads to a public health crisis. Therefore, detecting fake news is crucial for an effective management of the COVID-19 pandemic response. Studies have shown that machine learning models can detect COVID-19 fake news based on the content of news articles. However, the use of biomedical information, which is often featured in COVID-19 news, has not been explored in the development of these models. We present a novel approach for predicting COVID-19 fake news by leveraging biomedical information extraction (BioIE) in combination with machine learning models. We analyzed 1164 COVID-19 news articles and used advanced BioIE algorithms to extract 158 novel features. These features were then used to train 15 machine learning classifiers to predict COVID-19 fake news. Among the 15 classifiers, the random forest model achieved the best performance with an area under the ROC curve (AUC) of 0.882, which is 12.36% to 31.05% higher compared to models trained on traditional features. Furthermore, incorporating BioIE-based features improved the performance of a state-of-the-art multi-modality model (AUC 0.914 vs. 0.887). Our study suggests that incorporating biomedical information into fake news detection models improves their performance, and thus could be a valuable tool in the fight against the COVID-19 infodemic.
... Experts have recently warned against the dangers of deepfakes, a form of disinformation created by artificial intelligence. Specifically, deepfakes are highly realistic but synthetically generated video or audio representations of individuals created using artificial intelligence (Westerlund, 2019). They are often more striking, persuasive, and deceptive compared to textbased disinformation (Hameleers et al., 2020). ...
Full-text available
Deepfakes are a troubling form of disinformation that has been drawing increasing attention. Yet, there remains a lack of psychological explanations for deepfake sharing behavior and an absence of research knowledge in non-Western contexts where public knowledge of deepfakes is limited. We conduct a cross-national survey study in eight countries to examine the role of fear of missing out (FOMO), deficient self-regulation (DSR), and cognitive ability in deepfake sharing behavior. Results are drawn from a comparative survey in seven South Asian contexts (China, Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) and compare these findings to the United States, where discussions about deepfakes have been most relevant. Overall, the results suggest that those who perceive the deepfakes to be accurate are more likely to share them on social media. Furthermore, in all countries, sharing is also driven by the social-psychological trait – FOMO. DSR of social media use was also found to be a critical factor in explaining deepfake sharing. It is also observed that individuals with low cognitive ability are more likely to share deepfakes. However, we also find that the effects of DSR on social media and FOMO are not contingent upon users’ cognitive ability. The results of this study contribute to strategies to limit deepfakes propagation on social media.
The twenty-first century has seen technology become an integral part of human survival. Living standards have been affected as the technology continues to advance. Deep fake is a deep learning-powered application that recently appeared on the market. It allows to make fake images and videos that people cannot discern from the real ones and is a recent technique that allows swapping two identities within a single video. A wide range of factors, including communities, organizations, security, religions, democratic processes as well as personal lives, are being impacted by deep fakes. In the case of images and videos being presented to an organization as evidence, if the images or videos are altered, the entire truth will be transformed into a lie. It is inevitable that with every new and beneficial technology, some of its adverse effects will follow, causing world problems. There have been several instances in which deep fakes have alarmed the entire network. During the last few years, the number of altered images and videos has grown exponentially, posing a threat to society. Additionally, this paper explores where deep fakes are used, the impacts made by them, as well as the challenges and difficulties associated with deep fakes in this rapidly developing society.KeywordsDeepfakeDeepfake videosArtificial intelligence
Teaching data literacy topics, such as machine learning, to security studies students is difficult because there are limited security-related teaching materials (e.g. datasets, user friendly software) for instructors. To address this challenge, we conducted an exploratory study to evaluate an asynchronous training module and software prototype with 15 college students. A key finding from this study is the importance of a simple teaching software tool and security case studies. The module boosted knowledge of key concepts and awareness of ‘big data’ accountability issues. We also found that teaching data-science concepts – even at an elementary level – requires that students have basic proficiencies working with datasets and spreadsheets, which suggests the need to integrate these skills throughout security studies curricula. This research also highlights the importance of building partnerships with data-science instructors to integrate data-science literacy in security studies and intelligence studies.
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It is near-impossible for casual consumers of images to authenticate digitally-altered images without a keen understanding of how to “read” the digital image. As Photoshop did for photographic alteration, so to have advances in artificial intelligence and computer graphics made seamless video alteration seem real to the untrained eye. The colloquialism used to describe these videos are “deepfakes”: a portmanteau of deep learning AI and faked imagery. The implications for these videos serving as authentic representations matters, especially in rhetorics around “fake news.” Yet, this alteration software, one deployable both through high-end editing software and free mobile apps, remains critically under examined. One troubling example of deepfakes is the superimposing of women’s faces into pornographic videos. The implication here is a reification of women’s bodies as a thing to be visually consumed, here circumventing consent. This use is confounding considering the very bodies used to perfect deepfakes were men. This paper explores how the emergence and distribution of deepfakes continues to enforce gendered disparities within visual information. This paper, however, rejects the inevitability of deepfakes arguing that feminist oriented approaches to artificial intelligence building and a critical approaches to visual information literacy can stifle the distribution of violently sexist deepfakes.
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Corruption of the information ecosystem is not just a multiplier of two long-acknowledged existential threats to the future of humanity – climate change and nuclear weapons. Cyber-enabled information warfare has also become an existential threat in its own right, its increased use posing the possibility of a global information dystopia, in which the pillars of modern democratic self-government – logic, truth, and reality – are shattered, and anti-Enlightenment values undermine civilization as we know it around the world.
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There has been an increasing interest in developing methods for image representation learning, focused in particular on training deep neural networks to synthesize images. Generative adversarial networks (GANs) are used to apply face aging, to generate new viewpoints, or to alter face attributes like skin color. For forensics specifically on faces, some methods have been proposed to distinguish computer generated faces from natural ones and to detect face retouching. We propose to investigate techniques based on perceptual judgments to detect image/video manipulation produced by deep learning architectures. The main objectives of this study are: (1) To develop technique to make a distinction between Computer Generated and photographic faces based on Facial Expressions Analysis; (2) To develop entropy-based technique for forgery detection in Computer Generated (CG) human faces. The results show differences between emotions in both original and altered videos. These computed results were large and statistically significant. The results show that the entropy value for the altered videos is reduced comparing with the value of the original videos. Histograms of original frames have heavy tailed distribution, while in case of altered frames; the histograms are sharper due to the tiny values of images vertical and horizontal edges.
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With the rise of AI and deep learning techniques, fake digital contents have proliferated in recent years. Fake footage, images, audios, and videos (known as deepfakes) can be a scary and dangerous phenomenon, and can have the potential of altering the truth and eroding trust by giving false reality. A Proof of Authenticity (PoA) of digital media is critical to help eradicate the epidemic of forged content. Current solutions lack the ability to provide history tracking and provenance of digital media. In this paper, we provide a solution and a general framework using Ethereum smart contracts to trace and track the provenance and history of digital content to its original source-even if the digital content is copied multiple times. The smart contract utilizes hashes of the InterPlanetary File System (IPFS) used to store digital content and its metadata. Our solution focuses on video content, but the solution framework provided in this paper is generic enough and can be applied to any other form of digital content. Our solution relies on the principle that if the content can be credibly traced to a trusted or reputable source, the content can then be real and authentic. The full code of the smart contract has been made publicly available at Github.
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We now have almost no filters on information that we can access, and this requires a much more vigilant, knowledgeable reader. Learning false information from the web can have dire consequences for personal, social, and personal decision making. Given how our memory works and our biases in selecting and interpreting information, now more than ever we must control our own cognitive and affective processing. As examples: Simply repeating information can increase confidence in its perceived truth; initial incorrect information remains available and can continue to have an effect despite learning the corrected information; and we are more likely to accept information that is consistent with our beliefs. Information evaluation requires readers (a) to set and monitor their goals of accuracy, coherence, and completeness; (b) to employ strategies to achieve these goals; and (c) to value this time- and effort-consuming systematic evaluation. Several recommendations support a reasoned approach to fake news and manipulation.
In recent years, “fake news” has become a global issue that raises unprecedented challenges for human society and democracy. This problem has arisen due to the emergence of various concomitant phenomena such as 1) the digitization of human life and the ease of disseminating news through social networking applications (such as Facebook and WhatsApp); 2) the availability of “big data” that allows customization of news feeds and the creation of polarized so-called “filter-bubbles”; and 3) the rapid progress made by generative machine learning (ML) and deep learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos). There is a crucial need to combat the rampant rise of fake news and disinformation. In this article, we propose a high-level overview of a blockchain-based framework for fake news prevention and highlight the various design issues and consideration of such a blockchain-based framework for tackling fake news.
Fake news is nowadays an issue of pressing concern, given its recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in early 2017 to encourage the development of machine-learning-based classification systems for stance detection (i.e., for identifying whether a particular news article agrees, disagrees, discusses, or is unrelated to a particular news headline), thus helping in the detection and analysis of possible instances of fake news. This article presents a novel approach to tackle this stance detection problem, based on the combination of string similarity features with a deep neural network architecture that leverages ideas previously advanced in the context of learning-efficient text representations, document classification, and natural language inference. Specifically, we use bi-directional Recurrent Neural Networks (RNNs), together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article. These representations are then combined/compared, complemented with similarity features inspired on other FNC-1 approaches, and passed to a final layer that predicts the stance of the article toward the headline. We also explore the use of external sources of information, specifically large datasets of sentence pairs originally proposed for training and evaluating natural language inference methods to pre-train specific components of the neural network architecture (e.g., the RNNs used for encoding sentences). The obtained results attest to the effectiveness of the proposed ideas and show that our model, particularly when considering pre-training and the combination of neural representations together with similarity features, slightly outperforms the previous state of the art.
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: (i) detecting check-worthy claims and (ii) fact-checking claims. We develop supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features. For the check-worthiness estimation task, we focus on political debates, and we model the target claim in the context of the full intervention of a participant and the previous and following turns in the debate, taking into account contextual meta information. For the fact-checking task, we focus on answer verification in a community forum, and we model the veracity of the answer with respect to the entire question–answer thread in which it occurs as well as with respect to other related posts from the entire forum. We develop annotated datasets for both tasks and we run extensive experimental evaluation, confirming that both types of information—but especially contextual features—play an important role.
Discusses the dangers of fake news or misinformation being disseminated via online channels.