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A generic collusion attack optimization strategy for traditional spread-spectrum and quantization index modulation fingerprinting

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Abstract

Collusion attack is known to be a cost-effective attack against digital fingerprinting. Under the constraint that the fingerprint embedding algorithm and the host signal are unavailable to the adversaries, it is not very easy to perform efficient collusion attack. To solve this problem, we propose a generic collusion attack optimization (GCAO) strategy for traditional spread-spectrum (SS) and quantization index modulation (QIM) fingerprinting. First, we analyze the differential signal of two fingerprinted copies of the same content generated by popular fingerprint embedding algorithms, including both traditional SS-based embedder and QIM-based embedder. Based on the analysis of the differential signal, we construct a fingerprint forensic detector that identifies which embedder is applied and what the embedding parameters are adopted. Then, we improve the earlier proposed SANO collusion attack. Two components outlined above constitute a generic collusion attack optimization strategy. The simulation results show that the proposed forensic detector provides trustworthy performance: the detector can correctly identify the fingerprint embedding algorithm, and the probabilities of estimating the fingerprint length and position are higher than 86 % and 87 % for SS-based embedding and 84 % and 80 % for QIM-based embedding, respectively. The further experimental results illustrate that the proposed GCAO attack provides a better tradeoff between the probability of being undetected and the quality of the attacked copy.

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References

  1. Barni M, Bartolini F, De Rosa A, Piva A (2001) A new decoder for the optimum recovery of nonadditive watermarks. IEEE Trans Image Process 10(5):755–766

    Article  Google Scholar 

  2. Chang SG, Yu B, Vetterli M (2002) Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans Image Process 9(9):1522–1531

    Article  MathSciNet  Google Scholar 

  3. Chen B, Wornell GW (1999) An information-theoretic approach to the design of robust digital watermarking systems. In: IEEE international conference on acoustics, speech, and signal processing, vol 4. pp 2061–2064

  4. Chen B, Wornell GW (2001) Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans Inf Theory 47(4):1423–1443

    Article  MATH  MathSciNet  Google Scholar 

  5. Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687

    Article  Google Scholar 

  6. Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627

    Article  MATH  MathSciNet  Google Scholar 

  7. Donoho DL, Johnstone JM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3):425

    Article  MATH  MathSciNet  Google Scholar 

  8. Eggers JJ, Girod B (2001) Quantization effects on digital watermarks. Signal Process 81(2):239–263

    Article  Google Scholar 

  9. Feng H, Ling H, Zou F, Lu Z (2012) Svm-based anti-forensic method for spread-spectrum fingerprinting. Security and Communication Networks. doi:10.1002/sec.503

  10. Feng H, Ling H, Zou F, Yan W, Lu Z (2012) A collusion attack optimization strategy for digital fingerprinting. ACM Trans Multimed Comput Commun Appl 8(2S):20

    Google Scholar 

  11. Feng H, Ling H, Zou F, Yan W, Sarem M, Lu Z (2013) A collusion attack optimization framework toward spread-spectrum fingerprinting. Appl Soft Comput 13:3482–3493

    Article  Google Scholar 

  12. Feng H, Ling HF, Zou FH, Yan WQ, Lu ZD (2010) Optimal collusion attack for digital fingerprinting. In: Proceedings of ACM international conference on Multimedia (MM’10). ACM, New York, pp 767–770

  13. He S, Wu M (2005) Performance study on multimedia fingerprinting employing traceability codes. Lecture Notes in Computer Science, vol 3710. Springer, p 84

  14. He S, Wu M (2006) Joint coding and embedding techniques for multimedia fingerprinting. IEEE Trans Inf Forensic Secur 1(2):231–247

    Article  Google Scholar 

  15. Kirovski D, Mihcak MK (2005) Bounded gaussian fingerprints and the gradient collusion attack. In: IEEE international conference on acoustics, speech, and signal processing, vol 2. New York, pp 1037–1040

  16. Kiyavash N, Moulin P (2006) A framework for optimizing nonlinear collusion attacks on fingerprinting systems. In: 40th Annual Conference on Information Sciences and Systems. pp 1170–1175

  17. Kiyavash N, Moulin P (2009) Performance of orthogonal fingerprinting codes under worst-case noise. IEEE Trans Inf Forensic Secur 4(3):293–301

    Article  Google Scholar 

  18. Kuribayashi M., Kato H. (2010) Impact of rounding error on spread spectrum fingerprinting scheme. IEEE Trans Inf Forensic Secur 5(4):670–680

    Article  Google Scholar 

  19. Ling H, Feng H, Zou F, Yan W, Lu Z (2010) A novel collusion attack strategy for digital fingerprinting. In: Kim YS H-J, Barni M (eds) IWDW 2010, LNCS 6526. Springer, Heidelberg, pp 224–238

    Google Scholar 

  20. Ming-Shing H, Din-Chang T, Yong-Huai H (2001) Hiding digital watermarks using multiresolution wavelet transform. IEEE Trans Ind Electron 48(5):875–882

    Article  Google Scholar 

  21. Qiao L, Cox IJ (2007) Using perceptual models to improve fidelity and provide resistance to valumetric scaling for quantization index modulation watermarking. IEEE Trans Inf Forensic Secur 2(2):127–139

    Article  Google Scholar 

  22. Robertson MA, Stevenson RL (2005) Dct quantization noise in compressed images. IEEE Trans Circ Syst Video Tech 15(1):27–38

    Article  Google Scholar 

  23. Schuchman L (1964) Dither signals and their effect on quantization noise. IEEE Trans Commun Techn 12(4):162–165

    Article  Google Scholar 

  24. Stone H (1996) Analysis of attacks on image watermarks with randomized coefficients. NEC Research Institute, Tech. Rep 96-045

  25. Swaminathan A, He S, Wu M (2006) Exploring qim-based anti-collusion fingerprinting for multimedia. Proc SPIE 6072:698–709

    Google Scholar 

  26. Trappe W, Wu M, Wang ZJ, Liu KJR (2003) Anti-collusion fingerprinting for multimedia. IEEE Trans Signal Process 51(4):1069–1087

    Article  MathSciNet  Google Scholar 

  27. USC-SIPI (2012) The usc-sipi image database. Singal and Image Processing Institute, Ming Hsieh Electronic Engineering Department, University of Southern California, http://sipi.usc.edu/database/index.html

  28. Varna AL, Shan H, Swaminathan A, Min W (2009) Fingerprinting compressed multimedia signals. IEEE Trans Inf Forensic Secur 4(3):330–345

    Article  Google Scholar 

  29. Wang ZJ, Wu M, Trappe W, Liu KJRGroup-oriented fingerprinting for multimedia forensics. EURASIP Journal on Applied Signal Processing, vol 14, no 11. Hindawi Publishing Corporation, New York, p 2

  30. Wang ZJ, Wu M, Zhao HV, Trappe W, Liu KJR (2005) Anti-collusion forensics of multimedia fingerprinting using orthogonal modulation. IEEE Trans Image Process 14(6):804–821

    Article  Google Scholar 

  31. Wei ZH, Qin P, Fu YQ (1998) Perceptual digital watermark of images using wavelet transform. IEEE Trans Consum Electron 44(4):1267–1272

    Article  Google Scholar 

  32. Xiaojun Q, Bialkowski S, Brimley G (2008) An adaptive qim- and svd-based digital image watermarking scheme in the wavelet domain. In: IEEE international conference on image processing, pp 421–424

  33. Zhao HV, Min W, Wang ZJ, Liu KJR (2005) Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting. IEEE Trans Image Process 14(5):646–661

    Article  Google Scholar 

Download references

Acknowledgments

The authors appreciate the anonymous reviewers and the Associate Editor. This work is supported by NSF of China Grants 61301279, the Fundamental Research Funds for the Central Universities(WUT:2013-IV-068).

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Authors and Affiliations

  1. Key Laboratory of High Performance Ship Technology of Ministry of Education, School of Transportation, Wuhan University of Technology, Wuhan, Hubei, China

    Hui Feng

  2. Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, Hubei, China

    Langxiong Gan

  3. College of Computer Science, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Hefei Ling, Fuhao Zou & Zhengding Lu

Authors
  1. Hui Feng
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  2. Langxiong Gan
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  3. Hefei Ling
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  4. Fuhao Zou
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  5. Zhengding Lu
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Correspondence to Langxiong Gan.

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Feng, H., Gan, L., Ling, H. et al. A generic collusion attack optimization strategy for traditional spread-spectrum and quantization index modulation fingerprinting. Multimed Tools Appl 74, 6967–6988 (2015). https://doi.org/10.1007/s11042-014-1948-8

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  • DOI: https://doi.org/10.1007/s11042-014-1948-8

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