Countering Universal Image Tampering Detection with Histogram Restoration

  • Luyi Chen
  • Shilin Wang
  • Shenghong Li
  • Jianhua Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7809)

Abstract

In this paper, we point out state-of-the-art algorithm in natural image splicing detection, namely the transition probability matrix feature proposed by Shi, et al., can be attacked by modifying block discrete cosine transform (BDCT) coefficients without significantly degrading quality of the spliced image. BDCT coefficients of the spliced image are modified so that its distance to a close authentic image in feature space is minimized. The minimization is accomplished with a greedy algorithm. The modification makes the spliced image statistically similar to the authentic image so as to reduce the effectiveness of detection algorithm. The performance of the algorithm is evaluated on Columbia Image Splicing Detection Evaluation Dataset. With the proposed anti-forensics post processing, detection accuracy and true positive rate reduces to 69.4% and 62.5% respectively, while the processed images still maintain average peak signal-to-noise ratio (PSNR) at 42.22db.

Keywords

Anti-forensics Splicing Detection Information Security Gaussian Mixture Model Multivariate Statistics 

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References

  1. 1.
    Pevny, T., Fridrich, J.: Detection of Double-Compression in JPEG Images for Applications in Steganography. IEEE Transactions on Information Forensics and Security 3(2) (2008)Google Scholar
  2. 2.
    Fridrich, J., Soukal, D., Lukáš, J.: Detection of copy-move forgery in digital images. In: Digital Forensic Research Workshop (2003)Google Scholar
  3. 3.
    Ryu, S.-J., Lee, M.-J., Lee, H.-K.: Detection of copy-rotate-move forgery using zernike moments. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 51–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on Signal Processing 53(2), 758–767 (2005)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Shi, Y.Q., Chen, C., Chen, W.: A Natural Image Model Approach to Splicing Detection. In: The 9th workshop on Multimedia & Security, pp. 51–62. ACM, New York (2007)Google Scholar
  6. 6.
    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: The 7th workshop on Multimedia and Security. ACM (2005)Google Scholar
  7. 7.
    Kirchner, M., Böhme, R.: Hiding Traces of Resampling in Digital Images. IEEE Transactions on Information Forensics and Security 3(4) (2008)Google Scholar
  8. 8.
    Kirchner, M., Bohme, R.: Synthesis of color filter array pattern in digital images. In: Proc. of SPIE. SPIE, San Jose (2009)Google Scholar
  9. 9.
    Cao, G., et al.: Anti-Forensics of Contrast Enhancement in Digital Images. In: MM&Sec 2010. ACM, Rome (2010)Google Scholar
  10. 10.
    Stamm, M.C., et al.: Anti-Forensics of Jpeg Compression. In: ICASSP. IEEE (2010)Google Scholar
  11. 11.
    Shi, Y.Q., Chen, C.-H., Xuan, G., Su, W.: Steganalysis versus splicing detection. In: Shi, Y.Q., Kim, H.-J., Katzenbeisser, S. (eds.) IWDW 2007. LNCS, vol. 5041, pp. 158–172. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Pevný, T., Filler, T., Bas, P.: Using high-dimensional image models to perform highly undetectable steganography. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 161–177. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Ng, T.-T., Chang, S.-F.: A Dataset of Authentic and Spliced Image Blocks, ADVENT Technical Report, #203-2004-3, Columbia University (2004)Google Scholar
  14. 14.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luyi Chen
    • 1
  • Shilin Wang
    • 2
  • Shenghong Li
    • 1
  • Jianhua Li
    • 1
  1. 1.Dept. of Electrical EngineeringShanghai Jiaotong UniversityShanghaiChina
  2. 2.School of Information SecurityShanghai Jiaotong UniversityShanghaiChina

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