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    Analysis of Rolling Shutter Effect on ENF-Based Video Forensics
    (Ieee-Inst Electrical Electronics Engineers Inc, 2019) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) clue to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. These fluctuations in luminance can he captured by video recordings. Accordingly, the ENF can he estimated from such videos by the analysis of steady content in the video scene. When videos are captured by using a rolling shutter sampling mechanism, as is done mostly with CMOS cameras, there is an idle period between successive frames. Consequently, a number of illumination samples of the scene are effectively lost due to the idle period. These missing samples affect the ENF estimation, in the sense of the frequency shift caused and the power attenuation that results. This paper develops an analytical model for videos captured using a rolling shutter mechanism. This model illustrates how the frequency of the main ENF harmonic varies depending on the idle period length, and how the power of the captured ENF attenuates as idle period increases. Based on this, a novel idle period estimation method for potential use in camera forensics that is able to operate independently of video frame rate is proposed. Finally, a novel time-of-recording verification approach based on the use of multiple ENF components, idle period assumptions, and the interpolation of missing ENF samples is also proposed.
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    Detecting the Presence of ENF Signal in Digital Videos: A Superpixel-Based Approach
    (Ieee-Inst Electrical Electronics Engineers Inc, 2017) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    Electrical network frequency (ENF) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF-based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF-based forensic analysis. In this letter, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady super pixels, i.e., pixels that are most likely uniform in color, brightness, and texture, and intra-class similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 min and is independent of the camera sensor type, i.e., CCD or CMOS.
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    FACTORS AFFECTING ENF BASED TIME-OF-RECORDING ESTIMATION FOR VIDEO
    (Ieee, 2019) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    ENF (Electric Network Frequency) oscillates around a nominal value (50/60 Hz) due to imbalance between consumed and generated power. The intensity of a light source powered by mains electricity varies depending on the ENF fluctuations. These fluctuations can be extracted from videos recorded in the presence of mains-powered source illumination. This work investigates how the quality of the ENF signal estimated from video is affected by different light source illumination, compression ratios, and by social media encoding. Also explored is the effect of the length of the ENF ground-truth database on time of recording detection and verification.