Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Steganalysis Using Histogram Characteristic and Statistical Moments of Wavelet Subbands

웨이블릿 부대역의 히스토그램 특성과 통계적 모멘트를 이용한 스테그분석

  • Hyun, Seung-Hwa (School of Electronics Eng., Pusan National University) ;
  • Park, Tae-Hee (Dept. Mechatronics Eng., TongMyong University) ;
  • Kim, Young-In (Dept. Biomedical Eng., Pusan National University) ;
  • Kim, Yoo-Shin (School of Electronics Eng., Pusan National University) ;
  • Eom, Il-Kyu (School of Electronics Eng., Pusan National University)
  • 현승화 (부산대학교 전자공학과) ;
  • 박태희 (동명대학교 메카드로닉스공학과) ;
  • 김영인 (부산대학교 바이오메디컬공학과) ;
  • 김유신 (부산대학교 전자공학과) ;
  • 엄일규 (부산대학교 전자공학과)
  • Received : 2010.04.27
  • Published : 2010.11.25
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Abstract

In this paper, we present a universal steganalysis scheme. The proposed method extract features of two types. First feature set is extracted from histogram characteristic of the wavelet subbands. Second feature set is determined by statistical moments of wavelet characteristic functions. 3-level wavelet decomposition is performed for stego image and cover image using the Haar wavelet basis. We extract one features from 9 high frequency subbands of 12 subbands. The number of second features is 39. We use total 48 features for steganalysis. Multi layer perceptron(MLP) is applied as classifier to distinguish between cover images and stego images. To evaluate the proposed steganalysis method, we use the CorelDraw image database. We test the performance of our proposed steganalysis method over LSB method, spread spectrum data hiding method, blind spread spectrum data hiding method and F5 data hiding method. The proposed method outperforms the previous methods in sensitivity, specificity, error rate and area under ROC curve, etc.

본 논문은 스테가노그래피 알고리즘에 대한 블라인드 스테그분석 기법을 제안한다. 제안하는 스테그분석기법은 두 가지 형태의 특징 벡터를 추출한다. 첫 번째로, 영상에 정보를 은닉한 후 웨이블릿 부대역의 히스토그램 특성이 변한다는 것을 관찰하고 히스토그램의 위치 변화를 특징으로 이용한다. 두 번째로, 웨이블릿 특성 함수의 통계적 모멘트를 특징으로 이용한다. 첫번째 형태의 특징은 영상을 3-레벨 웨이블릿 변환하여 9개의 고주파 부대역에서 각각 하나의 특징을 추출하여 총 9개의 특징 벡터 얻는다. 두 번째 형태의 특징은 각 부대역별로 3차 모멘트까지 추출하여 39개의 특징 벡터를 얻는다. 총 48개의 특징 벡터를 교사학습을 이용하여 학습한 후 스테고 영상과 커버 영상을 분류한다. 다층 퍼셉트론 신경망 분류기를 이용하여 두 가지 형태의 특징을 입력으로 하여 삽입 데이터의 존재유무를 판별한다. 제안 방법의 성능을 평가하기 위하여 CorelDraw 데이터베이스 영상이 사용되었고 LSB 방법과 SS방법, blind SS방법, F5방법으로 다양한 삽입률의 스테고 영상을 생성하여 실험한다. 민감도와 특이도, 에러율, ROC 커브 면적 등을 이용하여 제안 방법이 기존의 스테그분석 방법보다 삽입 정보 유무를 검출하는데 효과적임을 보여준다.

Keywords

References

  1. H. Farid. "Detecting Hidden Messages Using Higher-order Statistical Models," in Proceedings of IEEE ICIP, pp.905-908, 2002.
  2. T. Holotyak, J. Fridrich and S. Voloshynovskiy. "Blind Statistical Steganalysis of Additive Steganography Using Wavelet Higher Order Statistics," CMS 2005, LNCS, vol. 3677, pp.273-274. Springer, Heidelberg, 2005.
  3. M. Goljan, J. Fridrich and T. Holotyak. "New Blind Steganalysis and its Implications," in Proceedings of Security, Steganography, and Watermarking of Multimedia Contents. SPIE, pp.1-13, 2006.
  4. J. Harmsen and W. Pearlman. "Steganalysis of Additive Noise Modelable Information Hiding," in Proceedings of Security, Steganography, and Watermarking of Multimedia Contents VI. SPIE, pp.131-142, 2003.
  5. 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," in Proceedings of Information Hiding Workshop, Barcelona, Spain, pp. 262-277, Jun. 2005.
  6. Y. Wang and P. Moulin. "Optimized Feature Extraction for Learning-based Image Steganalysis," IEEE Trans. Information Forensics and Security, vol. 2, no.1, pp.31-45, March, 2007. https://doi.org/10.1109/TIFS.2006.890517
  7. K. Sullivan, U. Madhow, S. Chandrasekaran and B. Manjunath. "Steganalysis for Markov cover data with applications to images," IEEE Trans. Information Forensics and security, vol.1, no.2, pp.275-287, Jun. 2006. https://doi.org/10.1109/TIFS.2006.873595
  8. G. Xuan, X. Cui, Y. Shi., et al. "JPEG Steganalysis Based on Classwise Non-principal Components Analysis and Multi-directional Markov Model," in Proceedings of IEEE ICME, pp903-906, 2007.
  9. Y. Shi, C. Chen and W. Chen. "A Markov Process Based Approach to Effective Attacking JPEG Steganography," IH 2006, LNCS, vol. 4437, pp.249-264. Springer, Heidelberg, 2007.
  10. A. Ker. "Steganalysis of LSB Matching in Grayscale Images," IEEE Signal processing letters, vol.12, no.6, pp.441-444, 2005. https://doi.org/10.1109/LSP.2005.847889
  11. C. Chen, Y. Shi and W. Chen, et al. "Statistical Moments Based Universal Steganalysis Using JPEG 2-D Array and 2-D Characteristic Function," in Proceedings of IEEE ICIP, pp.105-108, 2006.
  12. Q. Liu, A.H. Sung, Z. Chen and J. Xu. "Feature Mining and Pattern Classification for Steganalysis of LSB Matching Steganography in Grayscale Images," Pattern Recognition 41, pp.56-66, 2008. https://doi.org/10.1016/j.patcog.2007.06.005
  13. CorelDraw Software, www.corel.com.
  14. A. Westfeld. "High Capacity Despite Better Steganalysis (F5-A Steganographic Algorithm)," IH 2001, LNCS, vol.2137, Springer, Heidelberg, pp.289-302, 2001.
  15. Y. Sun, F. Liu, B. Liu and P. Wang. "Steganalysis Based on Difference Image," IWDW 2008, LNCS, vol.5450, pp.184-198, Springer, Heidelberg, 2009.
  16. A. Westfeld. "Roc Curves for Steganalysts," in Proceedings of the Third WAVILA Challenge( Wacha'07), Saint Malo (France), pp. 39-45 2007.
  17. WEKA tool http://www.cs.waikato.ac.nz/ml/weka/