ESPRIT-Hilbert-Based Audio Tampering Detection with SVM Classifier for Forensic Analysis via Electrical Network Frequency

Reis PMGI, Da Costa JPCL, Miranda RK, Del Galdo G (2017)

Publication Type: Journal article

Publication year: 2017

Journal

IEEE Transactions on Information Forensics and Security Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 12

Pages Range: 853-864

Article Number: 7775065

Journal Issue: 4

DOI: 10.1109/TIFS.2016.2636095

Abstract

Audio authentication is a critical task in multimedia forensics demanding robust methods to detect and identify tampered audio recordings. In this paper, a new technique to detect adulterations in audio recordings is proposed by exploiting abnormal variations in the electrical network frequency (ENF) signal eventually embedded in a questioned audio recording. These abnormal variations are caused by abrupt phase discontinuities due to insertions and suppressions of audio snippets during the tampering task. First, we propose an ESPRIT-Hilbert ENF estimator in conjunction with an outlier detector based on the sample kurtosis of the estimated ENF. Next, we use the computed kurtosis as an input for a support vector machine classifier to indicate the presence of tampering. The proposed scheme, herein designated as SPHINS, significantly outperforms related previous tampering detection approaches in the conducted tests. We validate our results using the Carioca 1 corpus with 100 unedited authorized audio recordings of phone calls.

Involved external institutions

Universidade de Brasília

Brazil (BR) Technische Universität Ilmenau

Germany (DE)

How to cite

APA:

Reis, P.M.G.I., Da Costa, J.P.C.L., Miranda, R.K., & Del Galdo, G. (2017). ESPRIT-Hilbert-Based Audio Tampering Detection with SVM Classifier for Forensic Analysis via Electrical Network Frequency. IEEE Transactions on Information Forensics and Security, 12(4), 853-864. https://doi.org/10.1109/TIFS.2016.2636095

MLA:

Reis, Paulo Max Gil Innocencio, et al. "ESPRIT-Hilbert-Based Audio Tampering Detection with SVM Classifier for Forensic Analysis via Electrical Network Frequency." IEEE Transactions on Information Forensics and Security 12.4 (2017): 853-864.

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