Abstract
MPEG-1 Audio Layer 3, more commonly referred to as MP3, is a popular audio format for consumer audio storage and a de facto standard of digital audio compression for the transfer and playback of music on digital audio players. MP3 audio forgery manipulations generally uncompress a MP3 file, tamper with the file in the temporal domain, and then compress the doctored audio file back into MP3 format. If the compression quality of doctored MP3 file is different from the quality of original MP3 file, the doctored MP3 file is said to have undergone double MP3 compression. Although double MP3 compression does not prove a malicious tampering, it is evidence of manipulation and thus may warrant further forensic analysis since, e.g., faked MP3 files can be generated by using double MP3 compression at a higher bit-rate for the second compression to claim a higher quality of the audio files. To detect double MP3 compression, in this paper, we extract the statistical features on the modified discrete cosine transform and apply a support vector machine to the extracted features for classification. Experimental results show that our designed method is highly effective for detecting faked MP3 files. Our study also indicates that the detection performance is closely related to the bit-rate of the first-time MP3 encoding and the bit-rate of the second-time MP3 encoding.
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Understanding online music stores, http://reviews.cnet.com/online-music-store-guide/.
IFPI DIGITAL MUSIC REPORT 2009, http://www.ifpi.org/content/library/DMR2009.pdf.
ISO/IEC JTC1/SC29/WG11 MPEG, IS11172-3, Information technology—coding of moving pictures and associated audio for digital storage media at up to about 1.5 Mbit/s, Part 3: Audio, 1992.
http://www.telos-systems.com/techtalk/hosted/Brandenburg_mp3_aac.pdf.
Bayram S, Sencar HT, Memon N. An efficient and robust method for detecting copy-move forgery. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, April 19–24, 2009.
Farid H. Image forgery detection, a survey. IEEE Signal Processing Magazine, March 16–25; 2009.
Fridrich J, Soukal D, Lukás J. Detection of copy move forgery in digital images. Proceedings of digital forensic research workshop, August; 2003.
Johnson MK, Farid H. Exposing digital forgeries by detecting inconsistencies in lighting. Proceedings of 7th ACM multimedia and security workshop, pp. 1–10; (2005).
Johnson MK, Farid H. Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensic Security 3(2):450–461; (2007).
Lin Z, He J, Tang X, Tang C-K. Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognit. 2009;42:2492–501.
Liu Q, Sung AH, Xu J, Ribeiro B. Image complexity and feature extraction for steganalysis of LSB matching steganography. In: Proceedings of 18th international conference on pattern recognition, ICPR, No. (2), pp 267–270; (2006).
Liu Q, Sung AH. Feature mining and nuero-fuzzy inference system for steganalysis of LSB matching steganography in grayscale images. In Proceedings of 20th international joint conference on artificial intelligence (IJCAI), pp 2808–2813; (2007).
Liu Q, Sung AH, Ribeiro B, Wei M, Chen Z, Xu J. Image complexity and feature mining for steganalysis of least significant bit matching steganography. Inf Sci. 2008;178(1):21–36.
Liu Q, Sung AH, Chen Z, Xu J. Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images. Pattern Recognit. 2008;41(1):56–66.
Liu Q, Sung AH, Qiao M. Improved detection and evaluation for JPEG steganalysis. In: Proceedings of 17th ACM multimedia conference, Beijing, Oct. 19–24, 2009, pp 873–876; (2009).
Liu Q, Sung AH, Qiao M. Novel feature mining for audio steganlysis. In: Proceedings of 17th ACM multimedia conference, Beijing, Oct. 19–24, 2009, pp 95–104; (2009).
Liu Q, Sung AH, Qiao M. Temporal derivative based spectrum and mel-cepstrum audio steganalysis. IEEE Trans Inf Forensic Security. 2009;4(3):359–68.
Liu Q, Sung AH, Qiao M, Chen Z, Ribeiro B. An improved approach to steganalysis of JPEG images. Inf Sci. 2010;180(9):1643–55.
Lukas J, Fridrich J, Goljan M. Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensic Security. 2006;1(2):205–14.
Mahdian B, Saic S. Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensic Security. 2008;3(3):529–38.
Popescu AC, Farid H. Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans Signal Process. 2005;53(2):758–67.
Qiao M, Sung AH, Liu Q. Steganalysis of MP3 Stegos. Proceedings of 22nd international joint conference on neural network, pp 2566–2571; (2009).
Vapnik VN. Statistical learning theory. New York: Wiley; 1998.
Yang R, Qu Z, Huang J. Detecting digital audio forgeries by checking frame offsets. MM&Sec 2008: 21–26; (2008).
Yang R, Shi YQ, Huang J. Defeating fake-quality MP3. MM&Sec 2009: 117–124; (2009).
Acknowledgments
The authors acknowledge the support for this research from the Institute for Complex Additive Systems Analysis (ICASA), a research division of New Mexico Tech. We are also grateful to the Editor and anonymous reviewers for their insightful comments and very helpful suggestions. Special thanks go to Elisa and Keegan of the Writing Center of the New Mexico Tech for their professional editing service.
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Liu, Q., Sung, A.H. & Qiao, M. Detection of Double MP3 Compression. Cogn Comput 2, 291–296 (2010). https://doi.org/10.1007/s12559-010-9045-4
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DOI: https://doi.org/10.1007/s12559-010-9045-4