전자공학회논문지CI (Journal of the Institute of Electronics Engineers of Korea CI)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

곱셉 잡음 첨가를 이용한 스테그분석의 성능 개선

Improvement of Steganalysis Using Multiplication Noise Addition

  • 박태희 (동명대학교 메카트로닉스공학과) ;
  • 엄일규 (동명대학교 메카트로닉스공학과)
  • 투고 : 2012.06.18
  • 심사 : 2012.07.03
  • 발행 : 2012.07.25
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초록

본 논문은 스테고 잡음의 분산을 확대함으로써 비밀 메시지의 존재를 감지하기 위한 개선된 스테그분석 방법을 제안한다. 먼저 주어진 영상에 스펙클 잡음을 곱하여 작은 스테고 잡음을 확대하고 소프트 임계치 방법에 의해 잡음이 제거된 영상을 추정한다. 스테고 잡음과 스펙클 잡음은 완전히 제거되지 않으므로 추정된 영상에는 잡음이 일부 존재하게 된다. 만약 주어진 영상이 커버 영상이라면, 잔존하는 잡음은 매우 적을 것이고, 스테고 영상이라면 상대적으로 큰 잡음을 가지게 될 것이다. 이때 스테고 영상의 경우 잡음의 영향으로 웨이블릿 영역에서 부모-자식 부대역의 통계적 의존성에 손상이 발생하게 된다. 따라서 본 논문에서는 이러한 특성을 이용하여 주어진 영상과 잡음이 제거된 영상의 차이로부터 조인트 통계 모멘트를 추출한다. 아울러 잡음이 제거된 영상에 대하여 4개의 통계적 모멘트를 추출하여 제안된 스테그분석에 사용한다. 추출된 특징 벡터는 MLP 분류기로 입력되어 학습된다. 제안 방법의 성능을 평가하기 위해 다양한 비트율의 임베딩 방법을 사용하였으며, 실험 결과 제안 기법은 기존의 방법에 비해 검출율과 정확도 측면에서 우수함을 확인할 수 있었다.

This paper proposes an improved steganalysis method to detect the existence of secret message. Firstly, we magnify the small stego noise by multiplying the speckle noise to a given image and then we estimate the denoised image by using the soft thresholding method. Because the noises are not perfectly eliminated, some noises exist in the estimated cover image. If the given image is the cover image, then the remained noise will be very small, but if it is the stego image, the remained noise will be relatively large. The parent-child relationship in the wavelet domain will be slighty broken in the stego image. From this characteristic, we extract the joint statistical moments from the difference image between the given image and the denoised image. Additionally, four statistical moments are extracted from the denoised image for the proposed steganalysis method. All extracted features are used as the input of MLP(multilayer perceptron) classifier. Experimental results show that the proposed scheme outperforms previous methods in terms of detection rates and accuracy.

키워드

참고문헌

  1. C. Abbas, C. Joan, C. Kevin and M. Paul, "Digital image steganography : Survey and analysis of current methods", Signal Processing, vol. 90, pp. 727-752, 2010. https://doi.org/10.1016/j.sigpro.2009.08.010
  2. X.Y. Luo, D.S. Wang, P. Wang and F.L. Liu, "A review on blind detection for image steganography," Signal Processing, vol. 88, no. 9, pp. 2138-2157, 2008. https://doi.org/10.1016/j.sigpro.2008.03.016
  3. S. Johri and A. Asthana, "An adaptive steganography technique for gray and colored images", Journal of Global Research in Computer Science, vol. 3, no. 3, pp. 41-45, 2012.
  4. A. Nissar, A.H. Mir, "Classification of steganalysis techniques: A study," Digital Signal Processing, vol. 20, no. 6, pp. 1758-1770, 2010. https://doi.org/10.1016/j.dsp.2010.02.003
  5. J. Fridrich, J. Kodovsky, "Rich Models for Steganalysis of Digital Images", IEEE Transactions on Information Forensics and Security, vol. 7, no. 3, pp. 868-882, 2012. https://doi.org/10.1109/TIFS.2012.2190402
  6. I. Avcibas, M. Kharrazi, N. Memon, B. Sankur, "Image Steganalysis with Binary Similarity Measures," Journal on Applied Signal processing, vol. 2005, no. 17, 2005.
  7. H. Farid, S. Lyu, "Detecting hidden messages using higher order statistics and support vector machines", Lecture Notes in Computer Science, vol. 2578, pp. 340-354, 2002.
  8. C. Liu, C. Ouyang, M. Guo and H. Chen, Harmsen and W. Pearlman. "Image steganalysis based on spatial domain and DWT domain features," Proceedings of Second International Conference on Network Security, Wireless Communications and Trusted Computing pp. 329-331, 2010.
  9. G. Xuan, Y. Q. Shi, J. Gao, D. Zou, C. Yang, Z. Zhang, P. Chai, C. Chen, and W. Chen, "Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions," Proceedings of Information Hiding Workshop, pp. 262-277, 2005.
  10. T.H. Park, S.H. Hyun, J.H. Kim and I.K. Eom, "Steganalysis Using Joint Moment of Wavelet Subbands," Journal of IEEK, vol. 48-SP, no. 3, pp. 71-78, 2011.
  11. A. Vishwa, S. Sharma, "Speckle noise reduction in ultrasound Images by wavelet thresholding," Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, 2012.
  12. D.L. Donoho, "De-noising by soft-thresholding," IEEE Trans on Information Theory, vol.41, pp.613-627, 1995. https://doi.org/10.1109/18.382009
  13. R.W. Buccigrossi, E.P.Simoncelli, "Image Compression via Joint Statistical Characterization in the Wavelet Domain", IEEE Trans Image Processing, vol. 8, pp. 1688-1701, Dec. 1999. https://doi.org/10.1109/83.806616
  14. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, Third Edtion, pp.1235, New York: Cambridge University Press, 2007.
  15. Y. Sun, F. Liu, B. Liu and P. Wang, "Steganalysis based on difference image," IWDW, LNCS, 2008.
  16. CorelDraw Software, www.CorelDraw.com.
  17. V. Jain, L. Kumar, M. M. Sharma, M. Sadiq and K. Rastogi, "Public-Key Steganography Based on Modifiend LSB Method", Journal of Global Research in Computer Science, vol. 3, no. 4, pp, 26-29, 2012.
  18. I.J. Cox, J. Kilian, T. Leighton and T. Shamoon, "Secure Spread Spectrum Watermarking for Multimedia," IEEE Trans. On Image Processing, vol. 6, pp. 1673-1687, 1997 https://doi.org/10.1109/83.650120
  19. A. Piva, M. barni, F. Bartolini and V. Cappellini, "DCT-based watermark recovering without resorting to the uncorrupted original image," Proceedings of IEEE International Conference on Image Processing, vol.1, pp.520-523, 1997.
  20. A. Westfeld, "High capacity despite better steganalysis(F5-A steganographic algorithm)," Proceedings of third Internaitonal Information Hiding Workshop, Lecture Notes in Computer Science, vol.2137, pp.289-302, 2001.
  21. WEKA, www.cs.waikato.ac.nz/ml/weka/
  22. I. H. Witten, E. Frank, Data Mining, Elservier, 2005.
  23. A.Westfeld, "ROC curves for steganalysis," Proceedings of the third WAVILA Challenge, pp. 39-45, 2007.