An Automated Approach for Electric Network Frequency Estimation in Static and Non-Static Digital Video Recordings

Abstract

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.

Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

“…Researchers have also employed image segmentation approaches for analyzing videos for ENF analysis. The authors of [24,26] proposed ENF estimation methods based on exploiting super-pixels. The method reported in [24] computed the average number of steady super-pixels instead of all steady pixels contained in a frame.…”

“…The method described in [26] is based on simple linear iterative clustering (SLIC) [41,42] for image segmentation. The authors employed the SLIC algorithm to generate regions of similar properties termed super-pixels whose average intensity exceeds a certain threshold to generate the mean intensity time series.…”

“…Studies have recently shown that ENF signals can also be extracted from video recordings made under the illumination of a light source powered by a mains grid [5,[24][25][26][27]. Fluorescent lights and incandescent bulbs used in indoor lighting fluctuate in light intensity at double the supply frequency, causing a nearly impossible to notice flickering that occurs in the illuminated environment.…”

“…Charge-coupled device (CCD) sensors commonly associated with global shutter mechanisms capture all the pixels in a video frame at the same time/instant. Unlike CCDs, complementary metal oxide semiconductor (CMOS) sensors often have a rolling shutter mechanism that causes the sensor to scan the rows of each frame sequentially so that various rows of the same frame are exposed at slightly different instants [26,28].…”

Image and Video Forensics

“…Hamdi et al [15] proposed an end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. Karantaidis et al [16] investigated the challenging problem of electric network frequency (ENF) estimation in static and non-static digital video recordings, designing an automated approach based on simple linear iterative clustering via the exploitation of areas with similar characteristics.…”