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Electric Network Frequency (ENF) is embedded in multimedia recordings if the recordings are captured with a device connected to power mains or placed near the power mains. It is exploited as a tool for multimedia authentication. ENF fluctuates stochastically around its nominal frequency at 50/60 Hz. In indoor environments, luminance variations captured by video recordings can also be exploited for ENF estimation. However, the various textures and different levels of shadow and luminance hinder ENF estimation in static and non-static video, making it a non-trivial problem. To address this problem, a novel automated approach is proposed for ENF estimation in static and non-static digital video recordings. The proposed approach is based on the exploitation of areas with similar characteristics in each video frame. These areas, called superpixels, have a mean intensity that exceeds a specific threshold. The performance of the proposed approach is tested on various videos of real-life scenarios that resemble surveillance from security cameras. These videos are of escalating difficulty and span recordings from static ones to recordings, which exhibit continuous motion. The maximum correlation coefficient is employed to measure the accuracy of ENF estimation against the ground truth signal. Experimental results show that the proposed approach improves ENF estimation against the state-of-the-art, yielding statistically significant accuracy improvements.

Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non-parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman-Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state-of-the-art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state-of-the-art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state-of-the-art methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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