In this paper, we present a new approach for audio tampering detection based on microphone classification. The underlying algorithm is based on a blind channel estimation, specifically designed for recordings from mobile devices. It is applied to detect a specific type of tampering, i.e., to detect whether footprints from more than one microphone exist within a given content item. As will be shown, the proposed method achieves an accuracy above 95% for AAC, MP3 and PCM-encoded recordings
The following paper presents our work on audio phylogeny with a focus on two application scenarios: audiovisual (A/V) archives and tampering detection. Starting from a set of near-duplicate audio files, our goal is to determine the processing history for the set, and detect the transformations that have been applied on each linked pair of nodes. Our approach targets AAC and MP3 encoding operations and is addressing both music and speech material
In this paper, we present a new algorithm for open-set microphone classification, which is based on a pre-existing blind channel estimation approach. The proposed method achieves a Rand index above 93% for AAC, MP3 and PCM-encoded recordings from eight different mobile devices
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