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Digital audio recording analysis: The Electric Network Frequency (ENF) Criterion
Abstract
This article reports on the Electric Network Frequency Criterion as a means of assessing the integrity of digital audio evidence. A brief description is given of phenomena that determine ENF variations. In most situations, to reach a non-authenticity opinion, the visual inspection of spectrograms and comparison with an ENF database are enough. A more detailed investigation, in the time domain, requires short time windows measurements and analyses. The stability of the ENF over geographical distances has been established by comparison of synchronized recordings made at different locations on the same network. A real case is presented, in which the ENF Criterion was used to investigate an audio file created with a secret surveillance system. By applying the ENF Criterion in forensic audio analysis, one can determine whether and where a digital recording has been edited, establish whether it was made at the time claimed, and identify the time and date of the registering operation.
... Over the past few years, electric network frequency (ENF) has been utilized as a tool in some applications in multimedia forensics. Analysis of the ENF is a forensic tool used to validate multimedia files and spot any attempts at manipulation [4] - [8]. The ENF is an electric power grid supply frequency and varies in time about its nominal value of 60 Hz in North America, and 50 Hz in Europe, Australia, and much of the rest of the world because of the inconsistencies caused by power network supply and demand [4], [9]. ...
... Analysis of the ENF is a forensic tool used to validate multimedia files and spot any attempts at manipulation [4] - [8]. The ENF is an electric power grid supply frequency and varies in time about its nominal value of 60 Hz in North America, and 50 Hz in Europe, Australia, and much of the rest of the world because of the inconsistencies caused by power network supply and demand [4], [9]. The nature of these inconsistencies can be observed to be random, unique per time, and usually the same across all locations connected by the same power grid. ...
... The nature of these inconsistencies can be observed to be random, unique per time, and usually the same across all locations connected by the same power grid. Consequently, an ENF signal recorded at any point in time while plugging to a certain power mains may serve as a reference ENF signal for the entire region serviced by that power grid for that period of time [4], [10]. The instantaneous values of ENF over time are considered ENF signals. ...
The electric network frequency (ENF) represents the transmission frequency of the electrical grid and fluctuates constantly around 50 Hz or 60 Hz, subject to the region. This constant fluctuation, caused by the continuous mismatch in power demand and supply, makes the ENF a unique signature, which can be utilized for several applications. According to studies, the ENF may be intrinsically implanted in audio recordings captured by digital audio recorders (e.g., microphone-based voice recorder) plugged into the mains supply or are situated close to sources of power and power transmission cable due to electromagnetic field interference originated from power source or acoustic hum and mechanical vibrations emitted by electrically operated devices such as regular household appliances. Recent studies further observed that video recordings made under an illumination source powered by main power can pick up the ENF signal. Following this discovery, several research efforts have been invested towards successful and accurate extraction of the ENF signal, and utilizing the ENF signal retrieved for several applications, including time stamp verification, audio/video authentication, location of recording estimation, power grid identification, estimation of camera read-out time, and video record synchronization. To the best of our knowledge, there has been no comprehensive survey on ENF-based multimedia forensics. Thus, in this paper, we present a comprehensive survey of studies conducted in this field, identifying several application specifics, current challenges, and future research directions.
... Esta aplicación es la más interdisciplinar porque requiere conocimientos técnicos de procesamiento de la señal para los que el fonetista no siempre está formado, pero sí pueden estarlo otros miembros de su grupo de trabajo, pues es bastante habitual trabajar en equipos interdisciplinares cuando se realizan peritajes forenses fonéticos. Para realizar el examen de autenticidad de una grabación, como detectar cortes y manipulaciones, es habitual un método conocido como análisis de la frecuencia de la red eléctrica; en inglés Electrical Network Frequency (ENF) (Grigoras, 2005;Cooper, 2009). Se trata de una técnica forense que consiste en comparar, por un lado, los cambios de frecuencia en la red eléctrica que existen de fondo en cualquier grabación con, por otro lado, los registros históricos de alta precisión de cambios de frecuencia de la red eléctrica que se tienen en una base de datos. ...
... Aunque es un método relativamente reciente, algunos investigadores lo consideran uno de los desarrollos más significativos en análisis forense de audio. En esta técnica, la señal de zumbido de la red se trata como si fuera una marca de agua digital dependiente del tiempo (esto es, sabemos que ocurrió en un momento determinado) que puede ayudar a establecer si una grabación digital se creó en el momento en que se supone que se hizo, y con ello se puede detectar cualquier edición en la misma (Grigoras, 2005). Esto se debe a que los generadores eléctricos emiten un zumbido continuo pero no uniforme, ya que depende de la demanda eléctrica de cada momento. ...
... Si el patrón que se encuentre en la señal de la voz (ruido blanco eléctrico) no se corresponde con los patrones que se tengan registrados, es que se ha manipulado de algún modo. Grigoras (2005) presenta un caso real en el que se utilizó el método -también llamado "criterio"-de la frecuencia de la red eléctrica para investigar un archivo de audio creado con un sistema de vigilancia secreto. Cooper (2009) confirma la utilidad de dicho método en Reino Unido y describe un enfoque automatizado para comparar las estimaciones de frecuencia de la red eléctrica encontradas en una grabación dubitada con una base de datos de valores frecuenciales de la red eléctrica. ...
Este trabajo revisa de manera crítica el ámbito de la lingüística aplicada conocido como fonética forense. Desde el propio nombre de esta disciplina, existen algunas controversias terminológicas, no solo acerca de cómo referirse a esta rama del saber, sino también sobre cuáles son –y cómo denominar– sus principales campos de actuación. Gracias a una pormenorizada revisión bibliográfica, describimos las cinco grandes áreas de aplicación de la fonética forense, haciendo énfasis en desmitificar posibles ideas erróneas sobre el alcance de esta disciplina. Asimismo, se ha hecho un esfuerzo por presentar los resultados de las investigaciones más recientes, sobre todo en el ámbito de la comparación forense de hablantes, que es la tarea más conocida y encargada con más frecuencia al perito forense. Para esta subárea nos centraremos en explicar las aproximaciones metodológicas actuales, así como los parámetros fonéticos más utilizados.
... The authentication of digital evidence is key for maintaining its credibility in court, particularly when considering the widespread availability of video and audio editing software. There are a number of methods an audio forensic examiner can utilise for authenticating digital audio evidence, one of which is the analysis of the Electric Network Frequency (ENF) [1]. This innovative method relies on the natural embedding of a low frequency signal within a digital audio file induced by recording the audio within close proximity to cables/devices carrying mains electricity, or by powering an audio recording device from a mains source with poor supply regulation, amongst other factors. ...
... There have been several publications based on ENF analysis, each offering their own view on the most effective and most efficient methods for implementing the technique within a forensic audio laboratory. Grigoras [1] is considered to be the seminal work, with his findings supporting the concept of mains frequency data being equal across a single network regardless of size. By collecting mains data in three Romanian cities connected to the same grid with a distance covering over 200km, the results were found to be identical across the network. ...
... Grigoras [1] also presents useful information in terms of the extraction of the ENF information from a suspect audio file, and the subsequent comparison to the database of mains frequencies. This requires a powerful audio editing software package with Fast Fourier Transform (FFT) capabilities. ...
Authenticating digital audio is a crucial task for audio forensic technicians (AFT) owing to increased use of digital multimedia in litigation. Analysing the electric network frequency (ENF), which can be unintentionally embedded when recording digital audio, is a critical authentication tool. Increasing use of digital media in court means growing demand for AFTs and raised demand for education. Thus, ENF analysis should be key in the education of future audio forensic technicians. A device for educational purposes has been designed to demonstrate the technology and procedures involved in ENF analysis, providing future AFTs practical experience in a key authentication technique.
... A type of this hidden information is the electric network frequency (ENF), which is a fluctuating signal within the power grid. This time-unique signal can be used to determine the time stamp, if it can be matched with data [1]. This study will focus on the presence of ENF in videos to determine a time of recording. ...
... This makes the ENF unique over time. These small variations can be visualized and consequently analyzed [1]. A visualization of 1-day ENF in the Netherlands is shown in Figure 1. ...
... This flickering can be extracted from an illuminated video as a signal, showing the variations in frequency created by the load on the grid (as can be seen in Figure 1). Therefore, the frequency variation can give a time estimation if reference material is available [1,4]. ...
In this research, the possibility of estimating the time a video was recorded at through electric network frequency is explored by examining various light sources in differentiating circumstances. This research focuses on videos made with smartphones. The smartphone cameras make use of an integrated complementary metal oxide semiconductor sensor. The filmed videos are analyzed using software, which employs a small electric network frequency (ENF) database to determine the time of recording of a video made in experimental circumstances. This research shows that in ideal circumstances, it is possible to determine the time stamp of a video recording made with a smartphone. However, it becomes clear that different light sources greatly influence the outcome. The best results are achieved with Halogen and Incandescent light sources, both of which also seem promising in less ideal circumstances. LED sources do work in ideal circumstances and, however, do not show much success in lesser circumstances. This research further demonstrates that there is potential in using ENF to determine a time stamp of recorded videos and provides validation on prior research on this topic. It proves usable in ideal circumstances with the presence of a clear light source on a white wall. With additional research, it has potential to become a feasible method to use for forensic settings in circumstances that are less ideal. Electric network frequency (ENF) can be visualized in video material due to light sources. Electric network frequency is a signal unique over time and thus can be used in time estimation for videos. Electric network frequency leads to a time of recording estimation for smartphone videos utilizing a rolling shutter. Time estimation using ENF can be done with different light sources and different phones.
... Based on the instantaneous difference between the supplied and demanded power, ENF exhibits variations over time [1]. These variations get integrated into audio recorded in the proximity of acoustic mains hum, or electromagnetic field of mains electricity [2]- [7]. ENF oscillations are also captured in video recordings of scenes lit by mains-powered lights without AC/DC converters within [8]- [24]. ...
... ENF oscillations are also captured in video recordings of scenes lit by mains-powered lights without AC/DC converters within [8]- [24]. The ENF fluctuations over time, that is, the ENF signal, can be extracted from these recordings using any frequency-domain or time-domain procedure outlined in [2], [9]. Consequently, the ENF can be utilized in several forensic and anti-forensic applications, including timeof-recording verification [6], [9], [20], [25], [26], media authentication [27]- [29], geo-location estimation [30], [31], multimedia synchronization [32], [33], and camera charac-terization [12], [34]. ...
In electric network frequency (ENF)-based video forensics, the analysis of videos captured by rolling shutter systems, where each row of a frame is exposed at different time instances, is critical. To gain the advantage of increased sampling frequency in these videos, in contrast to those captured by the global shutter where an entire frame is exposed at a time, the ENF-related luminance signal that is essential for ENF estimation is built by concatenating ENF-related luminance estimates across consecutive frames. However, this approach brings about some issues or phenomena owing to an idle period at the end of each frame. First, the ENF harmonics may be replaced by new ENF components and attenuated, thereby affecting the reliability of the ENF estimates from these videos. Another critical phenomenon is ENF reversal, which is yet to receive much research. This study comprehensively investigates this phenomenon to explore how and under what conditions the ENF is reversed. Further investigations led this study to examine how the ENF in the emerging components is mainly reconstructed from multiple ENF-related luminance harmonics, depending on the idle period. This helps identify reliable ENF components from which the ENF signal can be accurately estimated. In addition, it reveals the optimal idle periods for any ENF component. Using this outcome, this study also proposes a technique to enhance the effectiveness of an ENF component based on idle period modification. The experimental results show that the proposed method may boost the efficiency of an unreliable ENF component, outperforming the existing techniques.
... In areas with alternating currents, the ENF signal can be captured using recording devices. The uniqueness of the ENF signal in the audio file, and its abnormal variation due to editing or tampering, makes it a reliable indicator of the authenticity of the audio file [2][3][4][5][6]. We conducted a thorough investigation of Electrical Network Frequency (ENF)-based applications in media forensics. ...
... Since the discovery that 50 or 60 Hz electric network frequency (ENF) signals can be captured from recordings [2], numerous studies have used this discovery to evaluate the authenticity of audio files. ENF is a stable and unique signal in the electric network, and a method [3] was proposed to verify the authenticity of audio files by detecting the discontinuity of ENF. ...
The detection of audio tampering plays a crucial role in ensuring the authenticity and integrity of multimedia files. This paper presents a novel approach to identifying tampered audio files by leveraging the unique Electric Network Frequency (ENF) signal, which is inherent to the power grid and serves as a reliable indicator of authenticity. The study begins by establishing a comprehensive Chinese ENF database containing diverse ENF signals extracted from audio files. The proposed methodology involves extracting the ENF signal, applying wavelet decomposition, and utilizing the autoregressive model to train effective classification models. Subsequently, the framework is employed to detect audio tampering and assess the influence of various environmental conditions and recording devices on the ENF signal. Experimental evaluations conducted on our Chinese ENF database demonstrate the efficacy of the proposed method, achieving impressive accuracy rates ranging from 91% to 93%. The results emphasize the significance of ENF-based approaches in enhancing audio file forensics and reaffirm the necessity of adopting reliable tamper detection techniques in multimedia authentication.
... Enterkonnekte bir elektrik şebekesi üzerindeki tüm jeneratörlerin senkron çalışması sonucu şebeke üzerindeki tüm noktalarda aynı ENF değişimleri gözlenir [1]. ENF, elektrik şebeke gerilimi kaynaklı elektromanyetik alan ya da akustik gürültünün (akustik şebeke gürültüsü) var olduğu ortamlarda yapılan ses kayıtlarına istemsiz olarak girişim yapar [2][3][4][5][6][7]. Ayrıca, ENF, elektrik şebekesinden beslenen bir ışık kaynağı ile aydınlatılan ortamlarda yapılan video kayıtlarına gömülür [8][9][10][11][12][13][14][15][16][17][18][19]. ...
... Bu sebeple, bir ses ya da video dosyasının ENF tabanlı adli analiz uygulamalarının performansı kayıt dosyasının uzunluğu ile, dolayısıyla da dosyadan kestirimi sağlanan ENF zaman serisinin (ENF sinyalinin) uzunluğu ile doğru orantılıdır. Medya dosyalarından (ses ya da video) ENF sinyali kestiriminde sıklıkla tercih edilen en önemli yöntemlerden biri kısa zamanlı Fourier dönüşümü (Short-Time Fourier Transform -STFT) tabanlı yaklaşımdır[2,3,[22][23][24][25]. STFT yöntemi ile ENF sinyali kestiriminde, STFT pencere boyutu ve kaydırma miktarı parametrelerinin seçimi, medyadan elde edilen ENF sinyalinin doğruluğuna, dolayısıyla da ENF tabanlı adli analiz uygulamalarının performansına doğrudan etki edebilmektedir. ...
Hızla gelişmekte olan bilgisayar teknolojisi sayesinde çeşitli tekniklerle dijital ses, görüntü ve video dosyaları üzerinde modifikasyonlar yapılabilmektedir. Bu modifikasyonlar doğrudan dosya içeriğinde olabileceği gibi bazen de meta data üzerinde olabilmektedir. Bu bağlamda dijital medyaların adli analizi büyük önem arz etmektedir. Elektrik şebeke frekansı (ENF) tabanlı adli analiz yaklaşımı dosya bütünlük kontrolünde ve dosyaların kayıt zamanı tespitinde kullanılabilen önemli bir araçtır. ENF sinyali kestiriminde en çok tercih edilen yöntemlerden biri, kısa zamanlı Fourier dönüşümü (Short-Time Fourier Transform - STFT) temelli yaklaşımdır. STFT yönteminde, pencere boyutu ve kaydırma miktarı parametrelerinin seçimi büyük öneme sahip olup, kestirimi yapılan ENF sinyali doğruluğunu, dolayısıyla da ENF tabanlı adli analiz uygulamalarının performansını doğrudan etkileyebilmektedir. Bu çalışmada, STFT parametreleri seçiminin, ENF tabanlı dosya kayıt zamanı doğrulamada performansa ne derece etki ettiği araştırılmıştır. Farklı uzunluktaki ses dosyaları, çeşitli STFT pencere boyutu ve STFT kaydırma miktarlarına göre ayrı ayrı test edilerek karşılaştırmalı bir analiz yapılmıştır.
... The nature of these inconsistencies can be observed to be random, unique per time, and usually quite the same across all locations connected by the same power grid. Consequently, an ENF signal recorded at any location in time, connected to a certain mains power can serve as a reference ENF signal for the entire region serviced by that power network for that period of time [6,7]. ...
... The ENF's fluctuating/instantaneous values over time is considered as an ENF signal. An ENF signal is embedded in audio files created with devices connected to the mains power or located in environments where electromagnetic interference or acoustic mains hum is present [1,7,8]. This ENF signal can be estimated from the recordings using timedomain or frequency-domain techniques and utilized for various forensic and anti-forensic applications, such as time-stamp verification [5,8,9], audio/video authentication [10,11], location of recording estimation [12][13][14], power grid identification [15][16][17][18][19][20], and estimation of camera read-out time [21]. ...
The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (Tro) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the Tro to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach.
... An important characteristic of ENF signals, which makes them relevant to multimedia forensics, is that the ENF variations can be captured in media recordings made in places where there is electrical activity. In audio recordings, this is mainly due to electromagnetic influences and the power acoustic hum [1], [2]. In video recordings, this can be attributed to near-invisible flickering of electric lighting [3]. ...
... The similarity in the observations between ENF signals extracted from simultaneously recorded signals has motivated one of the early proposed ENF-based forensics applications: using the ENF traces to authenticate or identify the time-ofrecording of a signal [1]. Other proposed ENF-based applications include detection of tampering/modification in a media signal [7]- [10] [11], multimedia synchronization [12], [13], characterizing the video camera producing an ENF-containing video [14], and determining the location-of-recording among different grids [15], [16]. ...
div>The Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This has led to an emergence of several forensic applications based on the use of the ENF signature. Examples of such applications include estimating or verifying the time-of-recording of a media signal and inferring the power grid associated with the location in which the media signal was recorded. In this paper, we carry out a feasibility study to examine the possibility of using embedded ENF traces to pinpoint the location-of-recording of a signal within a power grid. In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces. To this purpose, a high-passed version of an ENF signal is extracted and it is demonstrated that the correlation between two such signals, extracted from recordings made in different geographical locations within the same grid, decreases as the distance between the recording locations increases. We harness this property of correlation in the ENF signals to propose trilateration based localization methods, which pinpoint the unknown location of a recording while using some known recording locations as anchor locations. We also discuss the challenges that need to be overcome in order to extend this work to using ENF traces in noisier audio/video recordings for such fine localization purposes.</div
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
In the previous chapters, multimedia forensics techniques based on the analysis of the data stream, i.e., the audio-visual signal, aimed at detecting artifacts and inconsistencies in the (statistics of the) content were presented. Recent research highlighted that useful forensic traces are also left in the file structure, thus offering the opportunity to understand a file’s life-cycle without looking at the content itself. This chapter is then devoted to the description of the main forensic methods for the analysis of image and video file formats.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Every camera model acquires images in a slightly different way. This may be due to differences in lenses and sensors. Alternatively, it may be due to the way each vendor applies characteristic image processing operations, from white balancing to compression.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Since the advent of smartphones, photography is increasingly being done with small, portable, multi-function devices. Relative to the purpose-built cameras that dominated previous eras, smartphone cameras must overcome challenges related to their small form factor. Smartphone cameras have small apertures that produce a wide depth of field, small sensors with rolling shutters that lead to motion artifacts, and small form factors which lead to more camera shake during exposure. Along with these challenges, smartphone cameras have the advantage of tight integration with additional sensors and the availability of significant computational resources. For these reasons, the field of computational imaging has advanced significantly in recent years, with academic groups and researchers from smartphone manufacturers helping these devices become more capable replacements for purpose-built cameras.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Chapters 3 and 4 have shown that PRNU is a very effective solution for source camera verification, i.e., linking a visual object to its source camera Lukas et al. (2006). However, in order to determine the source camera, there has to be a suspect camera in the possession of a forensics analyst which is often not the case.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Multimedia forensics is a societally important and technically challenging research area that will need significant effort for the foreseeable future. While the research community is growing and work like that in this book demonstrates significant progress, many challenges remain.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Empiricism is the notion that knowledge originates from sensory experience. Implicit in this statement is the idea that we can trust our senses. But in today’s world, much of the human experience is mediated through digital technologies. Our sensory experiences can no longer be trusted a priori. The evidence before us—what we see and hear and read—is, more often than not, manipulated.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
One particular disconcerting form of disinformation are the impersonating audios/videos backed by advanced AI technologies, in particular, deep neural networks (DNNs). These media forgeries are commonly known as the DeepFakes. The AI-based tools are making it easier and faster than ever to create compelling fakes that are challenging to spot. While there are interesting and creative applications of this technology, it can be weaponized to cause negative consequences. In this chapter, we survey the state-of-the-art DeepFake detection methods.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
We describe the threats posed by adversarial examples in an image forensic context, highlighting the differences and similarities with respect to other application domains. Particular attention is paid to study the transferability of adversarial examples from a source to a target network and to the creation of attacks suitable to be applied in the physical domain. We also describe some possible countermeasures against adversarial examples and discuss their effectiveness. All the concepts described in the chapter are exemplified with results obtained in some selected image forensics scenarios.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Photo-response non-uniformity (PRNU) is an intrinsic characteristic of a digital imaging sensor, which manifests as a unique and permanent pattern introduced to all media captured by the sensor. The PRNU of a sensor has been proven to be a viable identifier for source attribution and has been successfully utilized for identification and verification of the source of digital media.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
The literature of multimedia forensics is mainly dedicated to the analysis of single assets (such as sole image or video files), aiming at individually assessing their authenticity. Different from this, image provenance analysis is devoted to the joint examination of multiple assets, intending to ascertain their history of edits, by evaluating pairwise relationships. Each relationship, thus, expresses the probability of one asset giving rise to the other, through either global or local operations, such as data compression, resizing, color-space modifications, content blurring, and content splicing. The principled combination of these relationships unveils the provenance of the assets, also constituting an important forensic tool for authenticity verification. This chapter introduces the problem of provenance analysis, discussing its importance and delving into the state-of-the-art techniques to solve it.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
The rise of deep learning has led to rapid advances in multimedia forensics. Algorithms based on deep neural networks are able to automatically learn forensic traces, detect complex forgeries, and localize falsified content with increasingly greater accuracy. At the same time, deep learning has expanded the capabilities of anti-forensic attackers. New anti-forensic attacks have emerged, including those discussed in Chap. 10.1007/978-981-16-7621-5_14 based on adversarial examples, and those based on generative adversarial networks (GANs). In this chapter, we discuss the emerging threat posed by GAN-based anti-forensic attacks. GANs are a powerful machine learning framework that can be used to create realistic, but completely synthetic data. Researchers have recently shown that anti-forensic attacks can be built by using GANs to create synthetic forensic traces. While only a small number of GAN-based anti-forensic attacks currently exist, results show these early attacks are both effective at fooling forensic algorithms and introduce very little distortion into attacked images.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Physics-based methods anchor the forensic analysis in physical laws of image and video formation. The analysis is typically based on simplifying assumptions to make the forensic analysis tractable. In scenes that satisfy such assumptions, different types of forensic analysis can be performed. The two most widely used applications are the detection of content repurposing and content splicing. Physics-based methods expose such cases with assumptions about the interaction of light and objects, and about the geometric mapping of light and objects onto the image sensor.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Videos can be manipulated in a number of different ways, including object addition or removal, deep fake videos, temporal removal or duplication of parts of the video, etc. In this chapter, we provide an overview of the previous work related to video frame deletion and duplication and dive into the details of two deep-learning-based approaches for detecting and localizing frame deletion (Chengjiang et al. 2017) and duplication (Chengjiang et al. 2019) manipulations.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Images and videos are by now a dominant part of the information flowing on the Internet and the preferred communication means for younger generations. Besides providing information, they elicit emotional responses, much stronger than text does. It is probably for these reasons that the advent of AI-powered deepfakes, realistic and relatively easy to generate, has raised great concern among governments and ordinary people alike.
... Government agencies and research institutes of many countries have conducted ENF-related research and developmental work. This includes academia and government in Romania (Grigoras 2005, 2007), Poland (Kajstura et al. 2005, Denmark (Brixen 2007a(Brixen , b, 2008, the United Kingdom (Cooper 2008(Cooper , 2009a(Cooper , b, 2011 (Kim et al. 2017;Jeon et al. 2018), and Turkey (Vatansever et al. 2017(Vatansever et al. , 2019. Among the work studied, we can see two major groups of study. ...
... The earliest works on ENF-based forensic applications have focused on ENF-based time-of-recording authentication of audio signals (Grigoras 2005(Grigoras , 2007Cooper 2008;Kajstura et al. 2005;Brixen 2007a;Sanders 2008). The ENF pattern extracted from an audio signal should be very similar to the ENF pattern extracted from a power reference recording simultaneously recorded. ...
Every imaging sensor introduces a certain amount of noise to the images it captures—slight fluctuations in the intensity of individual pixels even when the sensor plane was lit absolutely homogeneously. One of the breakthrough discoveries in multimedia forensics is that photo-response non-uniformity (PRNU), a multiplicative noise component caused by inevitable variations in the manufacturing process of sensor elements, is essentially a sensor fingerprint that can be estimated from and detected in arbitrary images. This chapter reviews the rich body of literature on camera identification from sensor noise fingerprints with an emphasis on still images from digital cameras and the evolving challenges in this domain.
... They exhibit an identical trend within the same interconnected network. The ENF is a non-periodic signal, which can act as a fingerprint for digital forensics applications [2]. It can be embedded in digital audio recorded by devices plugged into the power mains or by devices placed near the electric outlets and power cables. ...
... The ENF was initially introduced by C. Grigoras [2,41] to attest to the authenticity of digital recordings, to determine the time they were recorded, and to indicate the area they were captured. In particular, when it comes to video recordings, ENF estimation can determine whether the multimedia content has undergone major alterations. ...
Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.
... The Electric Network Frequency (ENF) (Grigoras, 2005) serves as a "fingerprint", potentially embedded in multimedia content, such as audio recordings, that are captured in proximity to the power mains (Cooper, 2009). ENF fluctuates instantaneously around its nominal value of 60 Hz in the United States (US)/Canada or 50 Hz in the rest of the world. ...
The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Search, are developed for One-vs-All classification.
This process generates numerous predictions per sample, which are then compiled and used to train a shallow multi-label neural network specifically designed to model the fusion process, ultimately leading to the conclusive class prediction for each sample. Experimental findings reveal that both validation and testing accuracy outperform those of current state-of-the-art classifiers, underlining the effectiveness and robustness of the proposed methodology.
... Catalin Grigoras introduced the ENF criterion in forensic analysis [47]. Differences in power production and consumption cause the ENF variations. ...
The Electric Network Frequency (ENF) serves as a simple means to verify the authenticity of audio recordings. ENF variations contain crucial information, acting as a distinctive "fingerprint" when electronic devices are connected or located near power mains. A novel framework for ENF estimation is proposed. This approach alternates between the Least Absolute Deviation (LAD) regression for determining regression weights and objective function minimization with respect to frequency, adapting them within the context of the ℓ1 norm or the sum of ℓ1 norms of the approximation error. This framework is a direct consequence of Laplacian distributed noise. Goodness-of-fit tests are reported, indicating that the Laplacian noise hypothesis is more appropriate than the hypothesis of Gaussian noise in the benchmark ENF-WHU dataset. Extensive evaluation using audio recordings from the aforementioned dataset demonstrates the exceptional performance of the proposed framework outperforming state-of-the-art ENF estimation schemes. These findings provide compelling evidence for the efficacy of the proposed ENF estimation schemes as reliable prerequisites for detecting audio forgeries.
... The Electric Network Frequency (ENF) signal is employed as an authentication signature in a wide range of multimedia applications, starting from audio [1] and proceeding to video, e.g., [2]- [5]. The ENF is embedded in digital content captured by microphones, devices plugged into power mains, or near power sources. ...
Electric Network Frequency (ENF) acts as a fingerprint in multimedia forensics applications. In indoor environments , ENF variations affect the intensity of light sources connected to power mains. Accordingly, the light intensity variations captured by sensing devices can be exploited to estimate the ENF. A first optical sensing device based on a photodiode is developed for capturing ENF variations in indoor lighting environments. In addition, a device that captures the ENF directly from power mains is implemented. This device serves as a ground truth ENF collector. Video recordings captured by a camera are also employed to estimate the ENF. The camera serves as a second optical sensor. The factors affecting the ENF estimation are thoroughly studied. The maximum correlation coefficient between the ENF estimated by the two optical sensors and that estimated directly from power mains is used to measure the estimation accuracy. The paper's major contribution is in the disclosure of extensive experimental evidence on ENF estimation in scenes ranging from static ones capturing a white wall to non-static ones, including human activity. Keywords-Electric Network Frequency (ENF), optical sensor based on a photodiode, CMOS-based GoPro Hero8 camera, ENF estimation in video.
... This is employed as the most predominant analysis in forensic musicology. Unlike composition analysis which shows similarity through reductive approach this involves identification and differentiation on the basis melody, harmony and digital signals in exact form in which they were recorded [45]. This approach requires specific programs and software which are more related to audio domain of work. ...
Forensic musicology is the scientific study of music in a legal
context. It can be used to help identify the composer of a piece
of music, to determine the ownership of a copyright, or to resolve
disputes over the use of musical works. Forensic musicologists may
also be called upon to give expert testimony in court cases involving
questions of music.
Forensic musicology is a relatively new field, and there are no
formal educations or training requirements for becoming a forensic
musicologist. However, most forensic musicologists have advanced
degrees in music theory, musicology, or a related field, and many
also have experience working as professional musicians. Forensic
musicologists use their knowledge of musical composition, history,
and performance to answer questions raised in legal cases.
Forensic musicologists typically collaborate with attorneys,
judges, and other legal professionals to provide expert testimony
or analysis in court cases. In some cases, they may also be asked
to testify in front of a grand jury or give depositions. Forensic mu�sicologists may also be consulted by law enforcement agencies to
help identify unknown pieces of music or to authenticate record�ings. This review will focus on the application of forensic musicol�ogy in civil and criminal case
... In this work, we explore an unnoticeable location-related signature that may be inherently captured in images. This location-related signature comes from the power distribution network whose varying frequency over time is defined as the electric network frequency (ENF) signal [6]- [8]. The ENF signal over time in a multimedia recording reflects the behavior of the power grid at the time and the location in which the media recording was captured. ...
p>Published in IEEE Transactions on Information Forensics and Security (T-IFS) on 23 June 2022. This version on TechRxiv contains supplemental material.</p
... In recent years, there have been many studies on passive detection of audio tampering. The audio features used by these passive detection methods include audio statistical features such as voice pitch and formant (Chen et al., 2016;Xie et al., 2018;Yan et al., 2019a), Recording Device Information (Zeng et al., 2020;Zeng et al., 2021), Speaker information (Wang et al., 2020;Wang et al., 2021;Zeng et al., 2018), background noise (Pan et al., 2012) and Electronic Network Frequency (ENF) (Grigoras, 2005;Hua et al., 2016;Rodríguez et al., 2010). ENF is the power line transmission frequency (50 or 60HZ), and ENF is embedded in the audio in the form of buzzing when it is recorded (Hajj-Ahmad et al., 2018). ...
This paper proposes an audio tampering detection method based on the ENF phase and BI-LSTM network from the perspective of temporal feature representation learning. First, the ENF phase is obtained by discrete Fourier transform of ENF component in audio. Second, the ENF phase is divided into frames to obtain ENF phase sequence characterization, and each frame is represented as the change information of the ENF phase in a period. Then, the BI-LSTM neural network is used to train and output the state of each time step, and the difference information between real audio and tampered audio is obtained. Finally, these differences were fitted and dimensionally reduced by the fully connected network and classified by the Softmax classifier. Experimental results show that the performance of this method is better than the state-of-the-art approaches.
... In this work, we explore an unnoticeable location-related signature that may be inherently captured in an image. This signature comes from the power distribution network whose varying frequency over time is defined as the electric network frequency (ENF) signal [6]- [8]. The ENF signal over time in a multimedia recording reflects the behavior of the power grid at the time and the location where the media recording was captured. ...
Submitted to IEEE Transactions on Information Forensics and Security (T-IFS) in Dec. 2021. Under review.
... In multimedia recordings like audio and video, the ENF is embedded through multiple power-dependent sources. In the case of audio, the ENF is embedded through electromagnetic induction when the recorder is directly connected to the power grid [12]. For batterypowered devices, the ENF is embedded through the background hum [8]. ...
in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs
affect the reliability of large-scale multimedia surveillance systems
as attackers are able to manipulate the perception of live events.
For example, Deepfake audio/video attacks and frame duplication
attacks can cause significant security breaches. This paper proposes
a Lightweight Environmental Fingerprint Consensus based detection
of compromised smart cameras in edge surveillance systems
(LEFC). LEFC is a partial decentralized authentication mechanism
that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An
ENF signal carries randomly fluctuating spatio-temporal signatures,
which enable digital media authentication. With the proposed DLT
consensus mechanism named Proof-of-ENF (PoENF) as a backbone,
LEFC can estimate and authenticate the media recording and detect
byzantine nodes controlled by the perpetrator. The experimental
evaluation shows feasibility and effectiveness of proposed LEFC
scheme under a distributed byzantine network environment.
... There are techniques that discuss acquisition device identification [6], while the authors in [7] utilize footprints from the microphone to detect audio tampering through microphone classification. A breakthrough research in the area of audio authentication was the research that discussed the possibility of using the Electric Network Frequency (ENF) embedded in the recording as a means of time stamping and detecting tampering in audio recordings [8]. Since the introduction of ENF by Catalin Grigoras, several appealing techniques using the ENF signal for audio authentication have been proposed [9][10][11]. ...
In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words—or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time‐locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word‐final syllables. This is because word‐final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language.
Research Highlights
Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words.
Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words).
I show that a memory‐less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research.
Direct tests are needed to establish whether statistical learning leads to declarative memories for words.
Audio recordings of crime scenes are frequently utilized as digital evidence due to the universal usage of mobile phones, but they must first be authenticated before being used as evidence in court. The technique of identifying whether an audio recording is real or has been altered or manipulated is known as audio authenticity. In order to investigate and validate audio, this study provides a cutting-edge authentication technique that can distinguish the difference between real and fraudulent audio. Herein, we analyzed audio recording samples from different mobile handsets like iPhone, Samsung, One Plus etc. It is important to note that the proposed method authenticates audio files without regard to the audio content, i.e., without concern to the speaker or the speech. The audio recordings were subjected to manipulation that a human cannot recognize them auditory. The present results show that it is possible to verify the authentication of audio recordings generated through mobile phones or any recorder using metadata and the container structure of the recorded and edited audio file
Bu çalışmada ses kayıtları üzerinde yapılan manipülasyonların belirlenebilmesi için analiz edilmesi gereken görsel-işitsel ve dilbilimsel parametreler tasnif edilerek açıklanmış ve adli amaçlı yapılan incelemelere nasıl katkı sağlayacağı üzerinde durulmuştur. Ortaya konulan parametrelerle oluşturulan inceleme formu ile ses kayıtları üzerinde yapılmış olan manipülasyonların tespitinde etkin bir yöntemin ortaya konulması amaçlanmıştır. Önerilen yöntem, kesme (X), kopyalama (C) ve karıştırma (M) yöntemleriyle manipülasyon yapılarak, anlam bütünlüğü bozulmuş test kayıtları üzerinden sınanmıştır. Araştırmaya incelemeci olarak katılan ses inceleme alanında uzman gönüllü katılımcılardan, önerilen yöntemdeki parametrelerle oluşturulmuş inceleme formu aracılığı ile manipüle edilmiş test kayıtları üzerinde inceleme yapmaları istenmiştir. Katılımcılarca belirlenen bulgular üzerinde yapılan analizler sonucunda; katılımcıların önerilen yöntem ile test kayıtlarındaki manipülasyonların belirlenmesine yönelik bulgular elde ettiği görülmüştür. Karıştırma ve kopyalama yöntemiyle yapılan manipülasyonlarda katılımcılarca tespit edilen bulgu sayısının, kesme yöntemiyle yapılan manipülasyonlara nazaran daha yüksek olduğu görülmüştür. Öte yandan bu fark görsel ve işitsel incelemede görülürken, dilbilimsel incelemede görülmemektedir. Bu nedenle gerek kesme yöntemiyle gerekse diğer yöntemlerle yapılan manipülasyonların belirlenmesinde dilbilimsel inceleme ile elde edilen bulguların önerilen yöntemin tutarlılığına önemli katkılar sağladığı sonucuna varılmıştır.
Due to a constant imbalance between demand and supply of power, ENF (Electric Network Frequency) fluctuates around a nominal value of 50 or 60 Hz. These variations in ENF cause the luminance intensity of a mains-powered light source, having no AC/DC converter inside, also to fluctuate. As a result, a video of a scene illuminated by a mains-powered light source can be used to estimate these fluctuations. As a consequence, the ENF signal within the time period when the video was captured can be estimated. This work explores the effects of frame rate harmonics that emerge when a rolling shutter based approach is used for ENF estimation from videos captured using CMOS cameras. These harmonics are a problem, especially for videos whose frame rate is a divisor of the nominal ENF because the frame rate harmonics and the ENF harmonics overlap. It is discovered that a key reason for the presence of the harmonics is the inverse square law of light that results in some repeating patterns of luminance variation across frames. This paper presents an analysis of the effect of the inverse square law of light on ENF estimation. A technique for refined ENF-related luminance signal estimation is proposed that attenuates these frame rate harmonics. This enables more accurate ENF estimates. The work also proposes an approach to estimate ENF-related luminance waveform cycles within each video frame, and a method to compute the confidence score for the estimated cycles. It provides insight into the reliability of the extracted ENF signal from a video, in the sense of its usefulness for ENF forensics, and consequently for ENF detection, which is an important precursor to ENF-based video forensics.
Electric Network Frequency (ENF) continuously fluctuates around a nominal value (50/60 Hz) due to a persistent imbalance between supplied and demanded power. In certain circumstances, ENF gets intrinsically embedded into audio and video recordings and can be extracted from these recordings. Consequently, ENF can be used in a number of media forensic applications, such as verifying the time of recording of the media. In this work, a robust media time-stamping approach is proposed for media whose ENF content is relatively contaminated. It essentially entails two procedures: first, detecting all useful, i.e., considerably accurate, samples of an estimated ENF signal, and then applying an adapted normalized cross-correlation process that is designed for exploiting just the selected ENF portions based on a binary mask of the identified accurate samples. Experimental results show that the proposed approach provides significantly increased performance.
Industrial clustering can be considered as a result of two types of forces: the centripetal force, which encourages the concentration of the manufacturing activities, and centrifugal force, which acts in the opposite direction. To explain the agglomeration process, we develop an agent-based version of Krugman model (1991) which allows us considering less restrictive and real hypothesis on building up the model. In contrast to Krugman’s model which considers the workforce displacement between regions and assumes the firm’s size as an unlimited endogenous variable, the proposed model explicates the workers’ displacement at the level of firms in different regions and further introduces the effect of “ carrying capacity “ of firms, a concept very common in ecological models. We implement the agent-based model (ABM) with the goal of exploring the spatial distribution of firms across regions to see whether the workforce will concentrate. For this purpose, several scenarios were tested for different values of the key parameters of our ABM. The latter are: (1) the transport cost (τ), (2) the share of income spent on industrial goods (μ), (3) the elasticity of substitution (σ), (4) the initial nominal wage differential between regions (∆W) and (5) the carrying capacity of firms (Cap). Simulations have been carried under two initial conditions: an equal repartition of firms between regions and an unequal one. Simulation results suggest that reducing transport costs can have drastic effects on the disparity of industries. In case of high transport costs, decreasing the wage differential between regions reduces the spatial inequality. Further, the limited capacity of a firm to hire labor can slow down the migration process, which leads to a reduction in regional inequality.
The authenticity of digital audios has a crucial role when presented as evidence in the court of law or forensic investigations. Fake or doctored audios are commonly used for manipulation of facts and causing false implications. To facilitate passive-blind detection of forgery, the current paper presents a deep learning approach for detecting splicing in digital audios. It aims to eliminate the process of feature extraction from the digital audios by taking the deep learning route to expose forgery. A customized dataset of 4200 spliced audios is created for the purpose, using the publicly available Free Spoken Digit Dataset (FSDD). Unlike the other related approaches, the splicing is carried out at a random location in the audio clip that spans 1–3 s. Spectrograms corresponding to audios are used to train a deep convolutional neural network that classifies the audios as original or forged. Experimental results show that the model can classify the audios correctly with 93.05% classification accuracy. Moreover, the proposed deep learning approach also overcomes the drawbacks of feature engineering and reduces manual intervention significantly.KeywordsDigital forensicsAudio splicing detectionDeep learningAudio forensicsCNN
Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to tampering; and landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time–location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence–classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace; if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image’s capturing location.
There has been an increasing amount of work surrounding the Electric Network Frequency (ENF) signal, an environmental signature captured by audio and video recordings made in locations where there is electrical activity. ENF is the frequency of power distribution networks, 60 Hz in most of the Americas and 50 Hz in most other parts of the world. The ubiquity of this power signature and the appearance of its traces in media recordings motivated its early application toward time–location authentication of audio recordings. Since then, more work has been done toward utilizing this signature for other forensic applications, such as inferring the grid in which a recording was made, as well as applications beyond forensics, such as temporally synchronizing media pieces. The goal of this chapter is to provide an overview of the research work that has been done on the ENF signal and to provide an outlook for the future.
This article synthetically presents the possibilities of application of the ENF criterion during testing the authenticity of the audio recordings aimed to determine the date of their registration. The authors conducted a series of experiments to try to determine the parameters, which should be analysed in the recording so as to ensure the reliability and reproducibility of the test results. Anonymised analysis of subject materials from actual cases were provided for testing. The obtained results allow to conclude that the accuracy of the designation of the date of registration depends primarily on the duration of the recording. High value of obtained correlation does not prove the correctness of the designated date and time. The shorter the duration of the mains hum signal extracted from the recorded evidence, the greater the probability of fitting a random segment of the course of the power grid electricity frequency. The value of the SNR is irrelevant as long as the applied detection algorithm of the hum, in any given moment of time, effectively sets the value of the frequency of the hum.
In a forensic authenticity examination of an audio recording, being able to prove a recording is an original or some form of copy is of paramount importance. The consumer digital audio interface defined by the IEC60958-3 standard supports the Serial Copy Management System (SCMS) protocol. The significance of SCMS in the determination of original or copied status of digital recordings produced on 'SCMS-compliant' recorders forms the subject of this article.
Authenticity examinations of audio recordings
- J P French
Digital audio evidence analysis
- C Grigoras
Establishment of the individual characteristics of magnetic recording systems for identification purposes', Problems of Forensic Sciences
- S Molero
Voice identification & forensic audio & video analysis
- T Owen
The Relevance of Replay Transients in the Forensic Examination of Analogue Magnetic Tape Recordings, Publication No.16, London: Police Scientific Research and Development Branch
- D J Dean
Forensic audio systems
- C Grigoras