  Electric Network Frequency (ENF) acts as a fingerprint in multimedia
forensics applications. In indoor environments, ENF variations affect the
intensity of light sources connected to power mains. Accordingly, the light
intensity variations captured by sensing devices can be exploited to estimate
the ENF. A first optical sensing device based on a photodiode is developed for
capturing ENF variations in indoor lighting environments. In addition, a device
that captures the ENF directly from power mains is implemented. This device
serves as a ground truth ENF collector. Video recordings captured by a camera
are also employed to estimate the ENF. The camera serves as a second optical
sensor. The factors affecting the ENF estimation are thoroughly studied. The
maximum correlation coefficient between the ENF estimated by the two optical
sensors and that estimated directly from power mains is used to measure the
estimation accuracy. The paper's major contribution is in the disclosure of
extensive experimental evidence on ENF estimation in scenes ranging from static
ones capturing a white wall to non-static ones, including human activity.
