# Detecting the Presence of ENF Signal in Digital Videos: a Superpixel based Approach

## TL;DR

This paper presents a superpixel-based method to detect the presence of ENF signals in videos by analyzing luminance variations in steady regions, enabling effective forensic analysis even on short clips.

## Contribution

It introduces a novel ENF presence detection technique using superpixel-based estimations and intra-class similarity, applicable to short videos and independent of camera sensor type.

## Key findings

- Effective detection of ENF presence in videos as short as 2 minutes
- Method is independent of camera sensor type (CCD or CMOS)
- Achieves reliable ENF detection through superpixel analysis

## Abstract

ENF (Electrical Network Frequency) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF based forensic analysis. In this work, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady superpixels, i.e. pixels that are most likely uniform in color, brightness, and texture, and intraclass similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 minutes and is independent of the camera sensor type, i.e. CCD or CMOS.