|Improving location of recording classification using Electric Network Frequency (ENF) analysis
|SISY 2016 - IEEE 14th International Symposium on Intelligent Systems and Informatics
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.
Show full item record
checked on Feb 22, 2024
checked on Feb 21, 2024
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.