In this paper we present an audio tampering detection method based on the combination of blind microphone analysis and phase analysis of stable tones, e.g., the electrical network frequency (ENF). The proposed algorithm uses phase analysis to detect segments that might have been tampered. Afterwards, the segments are further analyzed using a feature vector able to discriminate among different microphone types. Using this combined approach, it is possible to achieve a significantly lower false-positive rate and higher reliability as compared to standalone phase analysis.

Authors: Cuccovillo, Luca; Mann, Sebastian; Aichroth, Patrick; Tagliasacchi, Marco; Dittmar, Christian
Affiliations: Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany; Politecnico di Milano, Milan, Italy(See document for exact affiliation information.)
AES Convention: 135 (October 2013) Paper Number: 8966
Publication Date: October 16, 2013 Import into BibTeX
Subject: Applications in Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=17016

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