Authors: Šarić, Željana
Žunić, Anastazia 
Zrnić, Tijana
Knežević, Milivoje
Despotović, Danica
Delić, Tijana
Title: Improving location of recording classification using Electric Network Frequency (ENF) analysis
Related Publication(s): Proceedings
Conference: SISY 2016 - IEEE 14th International Symposium on Intelligent Systems and Informatics
Issue Date: 2016
ISBN: 9781509028665
DOI: 10.1109/SISY.2016.7601517
Recently the Electric Network Frequency (ENF), one of the main traits of a power grid, had become increasingly popular in forensics since it is considered as a signature in multimedia recordings. By analyzing the ENF, it is possible to determine the time and location of a recording. In this paper, the ENF signals were classified using five different machine learning algorithms in order to detect the region of the origin of the ENF signals extracted from power and audio recordings coming from 10 different electric networks. Three sets of novel signal features are introduced and compared with the ones previously discussed in the literature. The improvement in the classification accuracy when a combination of the referent and novel feature sets was used ranges from 3% to 19% for the ENF signals extracted from power and audio recordings, respectively. Finally, the classifier with the highest achieved average accuracy was found to be Random Forest.
Publisher: IEEE

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