Skip to main content

Audio Tampering Detection Based on Quantization Artifacts

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

Included in the following conference series:

  • 1981 Accesses

Abstract

MP3 is one of the common formats in the recording equipments. The authenticity and integrity of MP3 audio is widely concerned. We analyzed quantization characteristic of MP3 encoding, and studied the effect of quantified in frame offset and non offset frame. Then we combined statistical characteristics of zero spectral coefficients before and after quantization. Finally, a tampering detection method based on quantitative characteristics is proposed. According to the disadvantage of existing frame offset method that cannot detect high rate compression, this paper in the quantization artifacts description according to the characteristics of the line frequency distribution used in front of the 16 band. We can further effectively solve tamper detection problem of high bit rate compression by studying 16 band quantization. The experimental results show that the accuracy rate of proposed detection method can reach up to 99 %, the compression rate of detection can reach 256 Kbps, and the complexity of compared with the existing methods is significantly reduced.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ikram, S., Malik, H.: Digital audio forensics using background noise. In: IEEE International Conference on Multimedia and Expo, vol. 41, no. 3, pp. 106–110 (2010)

    Google Scholar 

  2. Malik, H., Hong, Z.: Recording environment identification using acoustic reverberation. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 22, no. 10, pp. 1833–1836 (2012)

    Google Scholar 

  3. Rui, Y., Zhenhua, Q., Jiwu, H.: Detecting digital audio forgeries by checking frame offsets. In: International Multimedia Conference, Processing of the 10th ACM Workshop on Multimedia and Security, pp. 21–26 (2008)

    Google Scholar 

  4. Rui, Y., Zhenhua, Q., Jiwu, H.: Exposing MP3 audio forgeries using frame offsets. ACM Trans. Multimedia Comput. Commun. Appl. 8(2), 1651–1654 (2012)

    Google Scholar 

  5. Lv, Z., Hu, Y., Li, C.T., et al.: Audio forensic authentication based on MOCC between ENF and reference signals. In: IEEE China Summit and International Conference, pp. 427–431 (2013)

    Google Scholar 

  6. Su, H., Garg, R., Hajj-Ahmad, A., et al.: ENF analysis on recaptured audio recordings. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 32, pp. 3018–3022 (2013)

    Google Scholar 

  7. Garg, R., Varna, A.L., Hajjahmad, A., et al.: Seeing ENF: power-signature-based timestamp for digital multimedia via optical sensing and signal processing. IEEE Trans. Inf. Forensics Secur. 8(9), 1417–1432 (2013)

    Article  Google Scholar 

  8. Chen, J., Xiang, S., Huang, H.: Detecting and locating digital audio forgeries based on singularity analysis with wavelet package. Multimedia Tools Appl. 75, 2303–2325 (2016)

    Article  Google Scholar 

  9. Bianchi, T., Rosa, A.D., Fontani, M.: Detection and localization of double compression in MP3 audio tracks. EURASIP J. Inf. Secur. 1, 1–14 (2014)

    Google Scholar 

  10. Milani, S., Piazza, P.F., Bestagini, P.: Audio tampering detection using multimodal features. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). vol. 152a, pp. 4563–4567 (2014)

    Google Scholar 

  11. Korycki, R.: Time and spectral analysis methods with machine learning for the authentication of digital audio recordings. Forensic Sci. Int. 230(1–3), 117–126 (2013)

    Article  Google Scholar 

  12. Cuccovillo, L., Mann, S., Tagliasacchi, M.: Audio tampering detection via microphone classification. In: IEEE International Workshop on Multimedia Signal Processing, vol. 51, no. 7, pp. 177–182 (2013)

    Google Scholar 

  13. Zhaoqing, P., Yun, Z., Sam, K.: Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans. Broadcast. 61(2), 166–176 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61300055, 61672302), Zhejiang Natural Science Foundation (Grant No. LZ15F020002), Ningbo University Fund (Grant No. XKXL1405, XKXL1503) and K.C. Wong Magna Fund in Ningbo University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rangding Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Tao, B., Wang, R., Yan, D., Jin, C., Chen, Y., Zhang, L. (2016). Audio Tampering Detection Based on Quantization Artifacts. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48674-1_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics