Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Digital Forgeries Using Bispectral Analysis
Detecting Digital Forgeries Using Bispectral Analysis
Digital audio forensics: a first practical evaluation on microphone and environment classification
Proceedings of the 9th workshop on Multimedia & security
Detecting digital audio forgeries by checking frame offsets
Proceedings of the 10th ACM workshop on Multimedia and security
Evaluating digital audio authenticity with spectral distances and ENF phase change
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Proceedings of the 11th ACM workshop on Multimedia and security
Microphone Classification Using Fourier Coefficients
Information Hiding
Audio authenticity: detecting ENF discontinuity with high precision phase analysis
IEEE Transactions on Information Forensics and Security
Revealing real quality of double compressed MP3 audio
Proceedings of the international conference on Multimedia
Current Developments and Future Trends in Audio Authentication
IEEE MultiMedia
Zero-crossings of a wavelet transform
IEEE Transactions on Information Theory
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Exposing MP3 audio forgeries using frame offsets
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Issue on Multimedia Security
| Hi-index | 0.00 |
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.