Abstract
The electric network frequency (ENF) is a signature of power distribution networks that can be captured by multimedia signals recorded near electrical activities. This has led to the emergence of multiple forensic applications based on the use of ENF signals. Examples of such applications include validating the time-of-recording of an ENF-containing multimedia signal or estimating its recording location based on concurrent reference signals from power grids. In this paper, we examine a novel ENF-based application that infers the power grid in which the ENF-containing multimedia signal was recorded without relying on the availability of concurrent power references. We investigate features based on the statistical differences in ENF variations between different power grids to serve as signatures for the region-of-recording of the media signal. We use these features in a multiclass machine learning implementation that is able to identify the grid-of-recording of a signal with high accuracy. In addition, we explore techniques for building multiconditional learning systems that can adapt to changes in the noise environment between the training and testing data.
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