Countering JPEG anti-forensics based on noise level estimation

  • Hui Zeng
  • Jingjing Yu
  • Xiangui KangEmail author
  • Siwei Lyu
Research Paper


Quantization artifact and blocking artifact are the two types of well-known fingerprints of JPEG compression. Most JPEG forensic techniques are focused on these fingerprints. However, recent research shows that these fingerprints can be intentionally concealed via anti-forensics, which in turn makes current JPEG forensic methods vulnerable. A typical JPEG anti-forensic method is adding anti-forensic dither to DCT transform coefficients and erasing blocking artifact to remove the trace of compression history. To deal with this challenge in JPEG forensics, in this paper, we propose a novel countering method based on the noise level estimation to identify the uncompressed images from those forged ones. The experimental results show that the proposed method achieves superior performance on several image databases with only one-dimensional feature. It is also worth emphasizing that the proposed threshold-based method has explicit physical meaning and is simple to be implemented in practice. Moreover, we analyze the strategies available to the investigator and the forger in the case of that they are aware of the existence of each other. Game theory is used to evaluate the ultimate performance when both sides adopt their Nash equilibrium strategies.


game theory quantization artifact blocking artifact JPEG forensics anti-forensics noise level estimation 



This work was supported by National Natural Science Foundation of China (Grant Nos. U1536204, 61379155, 61332012), National Natural Science Foundation of Guangdong Province (Grant No. s2013020012788), and Special Funding for Basic Scientific Research of Sun Yat-sen University (Grant No. 6177060230).


  1. 1.
    Kwok C W, Au O C, Chui S H. Alternative anti-forensics method for contrast enhancement. In: Proceedings of the 10th International Conference on Digital-Forensics and Watermarking, Atlantic, 2011. 398–410Google Scholar
  2. 2.
    Milani S, Tagliasacchi M, Tubaro S. Antiforensics attacks to Benford’s law for the detection of double compressed images. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Vancouver, 2013. 3053–3057Google Scholar
  3. 3.
    Qian Z X, Zhang X P. Improved anti-forensics of JPEG compression. J Syst Softw, 2014, 91: 100–108CrossRefGoogle Scholar
  4. 4.
    Fan W, Wang K, Cayre F, et al. JPEG anti-forensics using non-parametric DCT quantization noise estimation and natural image statistics. In: Proceedings of the 1st ACM Workshop on Information Hiding and Multimedia Security, Montpellier, 2013. 117–122Google Scholar
  5. 5.
    Barni M, Tondi B. The source identification game: an information-theoretic perspective. IEEE Trans Inf Forens Secur, 2013, 8: 450–463CrossRefGoogle Scholar
  6. 6.
    Stamm M C, Lin W S, Liu K J R. Forensics vs. anti-forensics: a decision and game theoretic framework. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Kyoto, 2012. 1749–1752Google Scholar
  7. 7.
    Pevny T, Fridrich J. Detection of double-compression in JPEG images for applications in steganography. IEEE Trans Inf Forens Secur, 2008, 3: 247–258CrossRefGoogle Scholar
  8. 8.
    Lukas J, Fridrich J. Estimation of primary quantization matrix in double compressed JPEG images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, 2003. 1–17Google Scholar
  9. 9.
    Fu D D, Shi Y Q, Su W. A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Proceedings of SPIE 6505, Security, Steganography, and Watermarking of Multimedia Contents, San Jose, 2007. 65051LGoogle Scholar
  10. 10.
    He J F, Lin Z C, Wang L F, et al. Detecting doctored JPEG images via DCT coefficient analysis. In: Proceedings of 9th European Conference on Computer Vision, Graz, 2006. 423–435Google Scholar
  11. 11.
    Bianchi T, de Rosa A, Piva A. Improved DCT coefficient analysis for forgery localization in JPEG images. In: Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Prague, 2011. 2444–2447Google Scholar
  12. 12.
    Farid H. Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forens Secur, 2009, 4: 154–160CrossRefGoogle Scholar
  13. 13.
    Farid H. Digital Image Ballistics from JPEG Quantization. TR2006–583. 2008Google Scholar
  14. 14.
    Stamm M C, Tjoa S K, Lin W S, et al. Anti-forensics of JPEG compression. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, 2010. 1694–1697Google Scholar
  15. 15.
    Stamm M C, Liu K J R. Anti-forensics of digital image compression. IEEE Trans Inf Forens Secur, 2011, 6: 1050–1065CrossRefGoogle Scholar
  16. 16.
    Jiang Y W, Zeng H, Kang X G, et al. The game of countering JPEG anti-forensics based on the noise level estimation. In: Proceedings of Asian-Pacific Signal and Information Processing Association Annual Submit Conference, Taiwan, 2013. 1–9Google Scholar
  17. 17.
    Schaefer G, Stich M. UCID: an uncompressed color image database. In: Proceedings of SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia, San Jose, 2004. 472–480Google Scholar
  18. 18.
    Bas P, Filler T, Pevny T. Break our steganographic system: the ins and outs of organizing BOSS. In: Proceedings of International Conference on Information Hiding, Prague, 2011. 59–70Google Scholar
  19. 19.
    Lam E Y, Goodman J W. A mathematical analysis of the DCT coefficient distributions for images. IEEE Trans Image Process, 2000, 9: 1661–1666CrossRefzbMATHGoogle Scholar
  20. 20.
    Fan Z G, de Queiroz R L. Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process, 2003, 12: 230–235CrossRefGoogle Scholar
  21. 21.
    Kirchner M, Fridrich J. On detection of median filtering in digital images. In: Proceedings of SPIE, Electronic Imaging, Media Forensics and Security II, San Jose, 2010. 1–12Google Scholar
  22. 22.
    Lai S Y, Bohme R. Countering counter-forensics: the case of JPEG compression. In: Proceedings of International Conference on Information Hiding, Prague, 2011. 285–298Google Scholar
  23. 23.
    Valenzise G, Nobile V, Tagliasacchi M, et al. Countering JPEG anti-forensics. In: Proceedings of IEEE International Conference on Image Processing, Brussels, 2011. 1949–1952Google Scholar
  24. 24.
    Valenzise G, Tagliasacchi M, Tubaro S. Revealing the traces of JPEG compression anti-forensics. IEEE Trans Inf Forens Secur, 2013, 8: 335–349CrossRefGoogle Scholar
  25. 25.
    Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonl Phenom, 1992, 60: 259–268MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Li H D, Luo W Q, Huang J W. Countering anti-JPEG compression forensics. In: Proceedings of IEEE International Conference on Image Processing, Orlando, 2012. 241–244Google Scholar
  27. 27.
    Liu X H, Tanaka M, Okutomi M. Noise level estimation using weak textured patches of a single noisy image. In: Proceedings of IEEE International Conference on Image Processing, Orlando, 2012. 665–668Google Scholar
  28. 28.
    Shin D H, Park R H, Yang S, et al. Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans Consum Electr, 2005, 51: 218–226CrossRefGoogle Scholar
  29. 29.
    Pyatykh S, Hesser J, Zheng L. Image noise level estimation by principal component analysis. IEEE Trans Image Process, 2013, 22: 687–699MathSciNetCrossRefGoogle Scholar
  30. 30.
    Liu W, Lin W S. Additive white Gaussian noise level estimation in SVD domain for images. IEEE Trans Image Process, 2013, 22: 872–883MathSciNetCrossRefGoogle Scholar
  31. 31.
    Lyu S W, Pan X Y, Zhang X. Exposing region splicing forgeries with blind local noise estimation. Int J Comput Vis, 2014, 110: 202–221CrossRefGoogle Scholar
  32. 32.
    Osborne M J, Rubinstein A. A Course in Game Theory. Cambridge: MIT Press. 1994zbMATHGoogle Scholar
  33. 33.
    Hespanha J P. An Introductory Course in Noncooperative Game Theory. http://www.ece.ucsb. edu/~hespanha/Google Scholar
  34. 34.
    Grant M C, Boyd S P. Graph Implementations for Non-smooth Convex Programs. Recent Advances in Learning and Control. Heidelberg: Springer-Verlag, 2008. 95–110CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Hui Zeng
    • 1
  • Jingjing Yu
    • 1
  • Xiangui Kang
    • 1
    Email author
  • Siwei Lyu
    • 2
  1. 1.Guangdong Key Laboratory of Information Security, School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  2. 2.Department of Computer ScienceUniversity at Albany, SUNYAlbanyUSA

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