Detecting the Presence of ENF Signal in Digital Videos: a Superpixel based Approach
This addresses a critical need in video forensics for law enforcement or investigators to efficiently filter videos before ENF-based analysis, though it appears incremental as it builds on existing ENF detection concepts.
The paper tackles the problem of determining whether a digital video contains an ENF signal for forensic analysis by introducing a method based on multiple ENF estimations from steady superpixels and intraclass similarity, achieving operation on video clips as short as 2 minutes and independence from camera sensor type.
ENF (Electrical Network Frequency) 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 work, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e. pixels that are most likely uniform in color, brightness, and texture, and intraclass 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 minutes and is independent of the camera sensor type, i.e. CCD or CMOS.