  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) due to
changes in 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 be captured by video
recordings. Accordingly, ENF can be estimated from such videos by 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 ENF estimation, in the sense of the frequency shift caused and
the power attenuation that results. This work develops an analytical model for
videos captured using a rolling shutter mechanism. The 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 use of multiple ENF components, idle period assumptions and
interpolation of missing ENF samples is also proposed.
