Location Forensics Analysis Using ENF Sequences Extracted from Power and Audio Recordings

TL;DR

This paper introduces a novel method for determining the geographic origin of multimedia recordings by analyzing electrical network frequency (ENF) sequences extracted from power and audio signals, using SVM classification.

Contribution

It presents a new approach to location forensics by extracting ENF sequences from multimedia signals and classifying their origin with a support vector machine model.

Findings

High accuracy in classifying recording locations

Effective extraction of ENF sequences from diverse signals

Validation across multiple global grid locations

Abstract

Electrical network frequency (ENF) is the signature of a power distribution grid which represents the nominal frequency (50 or 60 Hz) of a power system network. Due to load variations in a power grid, ENF sequences experience fluctuations. These ENF variations are inherently located in a multimedia signal which is recorded close to the grid or directly from the mains power line. Therefore, a multimedia recording can be localized by analyzing the ENF sequences of that signal in absence of the concurrent power signal. In this paper, a novel approach to analyze location forensics using ENF sequences extracted from a number of power and audio recordings is proposed. The digital recordings are collected from different grid locations around the world. Potential feature components are determined from the ENF sequences. Then, a multi-class support vector machine (SVM) classification model is developed to validate the location authenticity of the recordings. The performance assessments affirm the efficacy of the presented work.