Statistical characterisation of MP3 encoders for steganalysis
Proceedings of the 2004 workshop on Multimedia and security
Digital audio forensics: a first practical evaluation on microphone and environment classification
Proceedings of the 9th workshop on Multimedia & security
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
Exposing MP3 audio forgeries using frame offsets
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Issue on Multimedia Security
Automatic telephone handset identification by sparse representation of random spectral features
Proceedings of the on Multimedia and security
Exposing digital audio forgeries in time domain by using singularity analysis with wavelets
Proceedings of the first ACM workshop on Information hiding and multimedia security
Detection and classification of double compressed MP3 audio tracks
Proceedings of the first ACM workshop on Information hiding and multimedia security
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MP3 is the most popular compressed audio format in our daily life but it can be doctored very easily by pervasive audio editing software. Thus it is necessary to develop authentication methods for MP3. Different from JPEG compression for image, MP3 compression has its own characteristics. Thus existing forensics methods for JPEG compression is unable to be applied to MP3 compression directly. In this paper, we propose a passive approach to detect doctored MP3 audio by checking frame offsets. As the audio samples are divided into frames to encode, each frame has its own frame offset after encoding. Forgeries lead to the broken of frame grids. So the frame offsets are good indication for locating forgeries, and the frame offsets can be detected by the identification of quantization characteristic. In this way, the doctored positions can be automatically located. Experimental results demonstrate the validity of the proposed approach on detecting some common forgeries, such as deletion, insertion, substitution and splicing. Under different bitrates, the detection ratios are above 94%. To the best of our knowledge, this piece of work is the first one to investigate digital forensics on MP3 format.