research-article

Exposing MP3 audio forgeries using frame offsets

Authors:

Jiwu HuangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 8, Issue 2S

Article No.: 35, Pages 1 - 20

https://doi.org/10.1145/2344436.2344441

Published: 20 September 2012 Publication History

Abstract

Audio recordings should be authenticated before they are used as evidence. Although audio watermarking and signature are widely applied for authentication, these two techniques require accessing the original audio before it is published. Passive authentication is necessary for digital audio, especially for the most popular audio format: MP3. In this article, we propose a passive approach to detect forgeries of MP3 audio. During the process of MP3 encoding the audio samples are divided into frames, and thus each frame has its own frame offset after encoding. Forgeries lead to the breaking of framing grids. So the frame offset is a good indication for locating forgeries, and it can be retrieved by the identification of the quantization characteristic. In this way, the doctored positions can be automatically located. Experimental results demonstrate that the proposed approach is effective in detecting some common forgeries, such as deletion, insertion, substitution, and splicing. Even when the bit rate is as low as 32 kbps, the detection rate is above 99%.

References

[1]

Boehm, R. and Westfeld, A. 2004. Statistical characterisation of mp3 encoders for steganalysis. In Proceedings of the 6th ACM Multimedia and Security Workshop. ACM.

Digital Library

[2]

Faac. 2012. Freeware advanced audio coder. http://www.audiocoding.com/faac.html.

[3]

Farid, H. 1999. Detecting digital forgeries using bispectral analysis. MIT AI Memo AIM-1657, MIT.

Digital Library

[4]

Fu, D., Shi, Y., and Su, W. 2007. A generalized benford's law for jpeg coefficients and its applications in image forensics. In Proceedings of SPIE Conference on Security, Steganography, and Watermarking of Multimedia Contents.

[5]

Grigoras, C. 2005. Digital audio recording analysis: The electric network frequency (enf) criterion. Int. J. Speech Lang. Law 2, 1, 63--76.

[6]

Herre, J. and Schug, M. 2000. Analysis of decompressed audio—The inverse decoder. In Proceedings of the 109th AES Convention.

[7]

Herre, J., Schug, M., and Geiger, R. 2002. Analysing decompressed audio with the inverse decoder—Towards an operative algorithm. In Proceedings of the 112th AES Convention.

[8]

ISO. 1992. Iso/iec international standard is 11172-3. Information technology—Coding of moving pictures and associated audio for digital storage media up to about 1.5 Mbit/s. http://www.iso.org/iso/catalouge_detail.htm&quest;csnumber=22412.

[9]

Kraetzer, C., Oermann, A., Dittmann, J., and Lang, A. 2007. Digital audio forensics: A first practical evaluation on microphone and environment classification. In Proceedings of the 9th ACM Multimedia and Security Workshop.

Digital Library

[10]

Lame 3.97. 2012. Mp3 encoder. http://lame.sourceforge.net.

[11]

Lukas, J. and Fridrich, J. 2003. Estimation of primary quantization matrix in double compressed jpeg images. In Proceedings of the Digital Forensic Research Workshop.

[12]

Painter, T. and Spanias, A. 2000. Perceptual coding of digital audio. Proc. IEEE 88, 4, 451--513.

[13]

Popescu, A. and Farid, H. 2004. Statistical tools for digital forensics. In Proceedings of the 6th International Workshop on Information Hiding.

Digital Library

[14]

Qu, Z., Luo, W., and Huang, J. 2008. A convolutive mixing model for shift double jpeg compression with application to passive image authentication. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]

Wang, Y. and Velermo, M. 2003. Modified discrete cosine transform—Its implications for audio coding and error concealment. AES J. 51, 1, 51--62.

[16]

Wang, Y., Yaroslavsky, L., Vilermo, M., and Vaananen, M. 2000. Some peculiar properties of the mdct. In Proceedings of the 16th IFIP World Computer Congress.

[17]

Yang, R., Qu, Z., and Huang, J. 2008. Detecting digital audio forgeries by checking frame offsets. In Proceedings of the 10th ACM Multimedia and Security Workshop. ACM.

Digital Library

Cited By

Su ZFang ZLian CZhang GLi M(2024)Audio splicing detection and localization using multistage filterbank spectral sketches and decision fusionMultimedia Systems10.1007/s00530-024-01288-x30:2Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1007/s00530-024-01288-x
Zhang KLiang SNie SHe SPan JZhang XMa HYi J(2022)A Robust Deep Audio Splicing Detection Method via Singularity Detection FeatureICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9746596(2919-2923)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9746596
Huang XLiu ZLu WLiu HXiang S(2020)Fast and Effective Copy-Move Detection of Digital Audio Based on Auto SegmentDigital Forensics and Forensic Investigations10.4018/978-1-7998-3025-2.ch011(127-142)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-3025-2.ch011
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Exposing MP3 audio forgeries using frame offsets

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 8, Issue 2S

Special Issue on Multimedia Security

September 2012

121 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2344436

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2012

Accepted: 01 August 2011

Revised: 01 July 2011

Received: 01 November 2010

Published in TOMM Volume 8, Issue 2S

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Citations

Cited By

Su ZFang ZLian CZhang GLi M(2024)Audio splicing detection and localization using multistage filterbank spectral sketches and decision fusionMultimedia Systems10.1007/s00530-024-01288-x30:2Online publication date: 25-Mar-2024
https://dl.acm.org/doi/10.1007/s00530-024-01288-x
Zhang KLiang SNie SHe SPan JZhang XMa HYi J(2022)A Robust Deep Audio Splicing Detection Method via Singularity Detection FeatureICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9746596(2919-2923)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9746596
Huang XLiu ZLu WLiu HXiang S(2020)Fast and Effective Copy-Move Detection of Digital Audio Based on Auto SegmentDigital Forensics and Forensic Investigations10.4018/978-1-7998-3025-2.ch011(127-142)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-3025-2.ch011
Boyarov ASiparov I(2020)Forensic Investigation of MP3 Audio RecordingsTheory and Practice of Forensic Science10.30764//1819-2785-2019-14-4-125-13614:4(125-136)Online publication date: 8-Jan-2020
https://doi.org/10.30764//1819-2785-2019-14-4-125-136
Huang XLiu ZLu WLiu HXiang S(2019)Fast and Effective Copy-Move Detection of Digital Audio Based on Auto SegmentInternational Journal of Digital Crime and Forensics10.4018/IJDCF.201904010411:2(47-62)Online publication date: 1-Apr-2019
https://doi.org/10.4018/IJDCF.2019040104
Zinemanas PFuentes MCancela PApolinário J(2018)An ENF-Based Audio Authenticity Method Robust to MP3 CompressionCircuits, Systems, and Signal Processing10.5555/3288801.328882437:11(4973-4992)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3288801.3288824
Zakariah MKhan MMalik H(2018)Digital multimedia audio forensicsMultimedia Tools and Applications10.1007/s11042-016-4277-277:1(1009-1040)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11042-016-4277-2
Zinemanas PFuentes MCancela PApolinário J(2018)An ENF-Based Audio Authenticity Method Robust to MP3 CompressionCircuits, Systems, and Signal Processing10.1007/s00034-018-0793-937:11(4973-4992)Online publication date: 8-Mar-2018
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Yakubu AMaddage NAtrey P(2017)Securing Speech Noise Reduction in Outsourced EnvironmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/310597013:4(1-24)Online publication date: 12-Aug-2017
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Reis PLustosa da Costa JMiranda RDel Galdo G(2017)ESPRIT-Hilbert-Based Audio Tampering Detection With SVM Classifier for Forensic Analysis via Electrical Network FrequencyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2016.263609512:4(853-864)Online publication date: 1-Apr-2017
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