  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.
