An efficient and robust method for detecting copy-move forgery
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
Novel stream mining for audio steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Improved detection and evaluation for JPEG steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Temporal derivative-based spectrum and mel-cepstrum audio steganalysis
IEEE Transactions on Information Forensics and Security
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An improved approach to steganalysis of JPEG images
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
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 format for audio storage and a de facto standard of digital audio compression for the transfer and playback. The flexibility of compression ratio of MP3 coding enables users to choose their customized configuration in the trade-off between file size and quality. Double MP3 compression often occurs in audio forgery, steganography and quality faking by transcoding an MP3 audio to a different compression ratio. To detect double MP3 compression, in this paper, we extract the statistical features on the modified discrete cosine transform, and apply support vector machines and a dynamic evolving neuron-fuzzy inference system to the extracted features for classification. Experimental results show that our method effectively and accurately detects double MP3 compression for both up-transcoded and down-transcoded MP3 files. Our study also indicates the potential for mining the audio processing history for forensic purposes.