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    A Review of Electrical Network Frequency (ENF) Based Applications in Media Forensics
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gunes, Ahmet Seyfullah; Vatansever, Saffet
    Electricity network frequency (ENF) is the frequency of electrical voltage transmitted by power distribution lines, with a nominal value of 50 Hz in most of the world and a nominal value of 60 Hz in the vast majority of America. The ENF makes continuous oscillations within certain limits around the nominal value depending on the supply-demand power imbalance in the network. These time-dependent changes in ENF are called the ENF signal. Although the ENF signal may show similarities in short time intervals, it becomes unique in large time intervals. The ENF signal is intrinsically integrated into audio and video recordings under certain conditions. The fact that the ENF signal shows different characteristics in different networks and is unique depending on time allows researchers to make inferences about the file content integrity, together with the location and time information of the audio and video files. In this study, it is discussed how to detect modifications in the file content and metadata using ENF within the scope of ENF-based forensic analysis of audio and video. In this context, existing ENF applications in the literature and the potential ENF usage areas are examined and analyzed. © 2023 IEEE.
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    A Survey of Source Camera Attribution Applications Using PRNU
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gunes, Ahmet Seyfullah; Vatansever, Saffet
    PRNU (Photo Response Non-Uniformity) is a sensor noise component that arises from the non-homogeneous structure of the semiconductor materials used in the production of image sensors, physical differences during the sensor manufacturing stage, and other unpredictable irregularities and defects. The PRNU characteristic differs among camera sensors, even within the same brand and model, and remains constant over time. In other words, it possesses a unique quality for each camera sensor. PRNU unintentionally embeds itself in every captured image, much like a fingerprint. Thanks to these features, PRNU enables various forensic applications for image and video, including source camera identification, file integrity verification, and forgery detection. This study presents an examination of PRNU-based source camera detection, which is the most common application of PRNU. Different scenarios in recordings of various resolutions and stabilized videos are discussed separately. © 2023 IEEE.