A high resolution ENF based multi-stage classifier for location forensics of media recordings

Abstract

Media recordings, when captured close to active power system components, are known to be influenced by the electromagnetic interference caused by those power grid components. This electromagnetic interference manifests itself in such media recordings in the form of time-varying frequency components around the electric network frequency (ENF) of the power grid. For example, the ENF of the Indian power grid has a nominal value 50Hz. Classification of a given media signal into the grid or region of recording using the electric network frequency (ENF) is vital in location forensics. In this work, we use power recordings and audio recordings captured from 12 different grids around the globe. To use the variations in the ENF from the media signals for region-of-recording classification, we propose a high resolution ENF extraction technique. We also propose the use of a multi-stage support vector machine (SVM) based classification system. We find that the proposed system outperforms the existing baseline scheme for region-of-recording classification, by yielding an improvement in the overall accuracy by 17.33%. � 2017 IEEE.