Category Attack for LSB Steganalysis of JPEG Images

  • Kwangsoo Lee
  • Andreas Westfeld
  • Sangjin Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4283)


In this paper, we propose a new method for the detection of LSB embedding in JPEG images. We are motivated by a need to further research the idea of the chi-square attack. The new method simply use the first-order statistics of DCT coefficients, but is more powerful to detect the random embedding in JPEG images. For evaluation, we used versions of Jsteg and Jphide with randomized embedding path to generate stego images in our experiments. In results, the proposed method outperforms the method of Zhang and Ping and is applicable to Jphide. The detection power of both proposed methods is compared to the blind classifier by Fridrich that uses 23 DCT features.


True Positive Rate Cover Image Secret Message Stego Image Lena Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Katzenbeisser, S., Petitcolas, F.A.P.: Information Hiding - techniques for steganography and digital watermarking. Artech House Books (1999)Google Scholar
  2. 2.
    Westfeld, A., Pfitzmann, A.: Attacks on steganographic systems. In: Pfitzmann, A. (ed.) IH 1999. LNCS, vol. 1768, pp. 61–76. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Provos, N., Honeyman, P.: Detecting Steganographic Content on the Internet, CITI Technical Report 03–11 (2001)Google Scholar
  4. 4.
    Fridrich, J., Goljan, M., Hogea, D.: Attacking the OutGuess. In: Proc. of the ACM Workshop on Multimedia and Security 2002, Juan-les-Pins, France, December 6 (2002)Google Scholar
  5. 5.
    Fridrich, J., Goljan, M., Soukal, D.: Higher-Order Statistical Steganalysis of Palette Images. In: Proc. of EI SPIE, Santa Clara, CA, January 2003, pp. 178–190 (2003)Google Scholar
  6. 6.
    Lyu, S., Farid, H.: Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines. In: Petitcolas, F.A.P. (ed.) IH 2002. LNCS, vol. 2578, pp. 340–354. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Zhang, T., Ping, X.: A Fast and Effective Steganalytic Technique against Jsteg-like Algorithms. In: ACM Symposium on Applied Computing, Florida, USA, March 9–12 (2003)Google Scholar
  8. 8.
    Fridrich, J.: Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 67–81. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Ker, A.D.: Improved Detection of LSB Steganography in Grayscale Images. In: Fridrich, J. (ed.) IH 2004. LNCS, vol. 3200, pp. 97–115. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Steganographic Tool Lists,
  11. 11.
  12. 12.
    Latham, A., Jphide, JPSeek,
  13. 13.
    Provos, N.: OutGuess – Universal Steganography,
  14. 14.
    CBIR Image Database, University of Washington,
  15. 15.
    R: A Language and Environment for Statistical Computing, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kwangsoo Lee
    • 1
  • Andreas Westfeld
    • 2
  • Sangjin Lee
    • 1
  1. 1.Center for Information Security Technologies (CIST)Korea UniversitySeoulKorea
  2. 2.Institute for System ArchitectureTechnische Universität DresdenDresdenGermany

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