Exposing digital audio forgeries in time domain by using singularity analysis with wavelets

Exposing digital audio forgeries in time domain is a significant research issue in the audio forensics community. In this paper, we develop an audio forensics method to detect and locate audio forgeries in time domain (including deletion, insertion, substitution and splicing) by analyzing singularity points of audio signals after performing discrete wavelet packet decomposition. Firstly, we observe and point out that a forgery operation in time domain will often generate a singularity point because the correlation property of those samples close to the tampering position has been degraded. Furthermore, we investigate and find that the singularity point resulted from a tampering operation often stays alone while those inherent singularity points in the original signal usually staying in the form of group. Finally, we propose an approach to expose audio forgeries in time domain by introducing Mallat et al.'s wavelet singularity analysis method and making a difference between a forged point and the inherent singularity points. Extensive experimental results have shown that the proposed scheme can better identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.