Electric Network Frequency Optical Sensing Devices
This work addresses multimedia forensics applications by providing a method for ENF estimation, but it is incremental as it builds on existing ENF fingerprinting concepts with new sensor implementations.
The paper tackled the problem of estimating Electric Network Frequency (ENF) from indoor lighting environments using optical sensors, achieving a maximum correlation coefficient between optical sensor estimates and ground truth from power mains.
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.