Audio tampering detection via microphone classification

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.