Steganalysis with CNN Using Multi-channels Filtered Residuals

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10602)

Abstract

In the current study of steganalysis, Convolutional Neural Network (CNN) have attracted many scholars’ attention. Recently, some effective CNN architectures have been proposed with better results than traditional Rich Models with Ensemble Classifiers. Inspired by the idea that Rich Models use various types of sub-models to enlarge different characteristics between cover and stego features, a scheme based on multi-channels filtered residuals is proposed for digital image steganalysis in this paper. This paper mainly focus on the stage of image processing, 3 high-pass filtered image residuals are fed to a deep CNN architecture to make full use of the great nonlinear curve fitting capability. As known, deep learning is powerful in pattern recognition, most previous networks only use single type of filtered residuals in steganalysis, varied high-pass filtered residuals can offer stronger features for CNN in this paper. After filtering, the residuals are superposed into a multi-channels residual map before training, this measure can involve a joint optimization of CNN’s parameters. But single residual map has no such effect. The experiment results prove that it’s an efficient way to provide a better detection performance, achieving an accuracy of 82.02% on Cropped-BOSSBase-1.01 dataset.

Keywords

Steganalysis CNN Multi-channels Residual 

Notes

Acknowledgments

This work is supported by the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Fundamental Research Funds for the Central Universities (No. 16lgjc83), and Scientific and Technological Achievements Transformation Plan of Sun Yat-sen University.

References

  1. 1.
    Fu, Z., Huang, F., Sun, X., Vasilakos, A. and Yang, C.-N.: Enabling semantic search based on conceptual graphs over encrypted outsourced data. IEEE Trans. Serv. Comput. 1, PP (2016). 5555, doi: 10.1109/TSC.2016.2622697
  2. 2.
    Sharp, T.: An implementation of key-based digital signal steganography. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 13–26. Springer, Heidelberg (2001). doi: 10.1007/3-540-45496-9_2 CrossRefGoogle Scholar
  3. 3.
    Luo, W., Huang, F., Huang, J.: Edge adaptive image steganography based on lsb matchingrevisited. IEEE Trans. Inf. Forensics Secur. 5(2), 201–214 (2013)Google Scholar
  4. 4.
    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). doi: 10.1007/978-3-642-16435-4_13 CrossRefGoogle Scholar
  5. 5.
    Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1 (2014)CrossRefGoogle Scholar
  6. 6.
    Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: IEEE International Conference on Image Processing, pp. 4206–4210 (2014)Google Scholar
  7. 7.
    Yuan, C., Xia, Z., Sun, X.: Coverless image steganography based on SIFT and BOF. J. Internet Technol. 18(2), 435–442 (2017)Google Scholar
  8. 8.
    Xia, Z., Wang, X., Sun, X., Wang, B.: Steganalysis of least significant bit matching using multi-order differences. Secur. Commun. Netw. 7(8), 1283–1291 (2013)CrossRefGoogle Scholar
  9. 9.
    Xia, Z., Wang, X., Sun, X., Liu, Q., Xiong, N.: Steganalysis of LSB matching using differences between nonadjacent pixels. Multimedia Tools Appl. 75(4), 1947–1962 (2014)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z., Yang, C., Chen, B., Sun, X., Liu, Q., Wu, Q.: Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans. Inf. Syst. E99-D(6), 1531–1540 (2016)CrossRefGoogle Scholar
  11. 11.
    Zhou, Z., Wang, Y., Wu, Q., Yang, C., Sun, X.: Effective and efficient global context verification for image copy detection. IEEE Trans. Inf. Forensics Secur. 12(1), 48–63 (2017)CrossRefGoogle Scholar
  12. 12.
    Sullivan, K., Madhow, U., Chandrasekaran, S., Manjunath, B.S.: Steganalysis of spread spectrum data hiding exploiting cover memory. In: Proceedings of Security, Steganography, and Watermarking of Multimedia Contents VII, San Jose, California, USA, 17–20 January 2005, pp. 38–46 (2005)Google Scholar
  13. 13.
    Fridrich, J., Kodovský, J., Holub, V., Goljan, M.: Steganalysis of content-adaptive steganography in spatial domain. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 102–117. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24178-9_8 CrossRefGoogle Scholar
  14. 14.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  15. 15.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  16. 16.
    Qian, Y., Dong, J.,Wang,W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Proceedings of SPIE, vol. 9409, pp. 94090J-1–94090J-10 (2015)Google Scholar
  17. 17.
    Pibre, L., Pasquet, J., Ienco, D., Chaumont, M.: Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch. In: Media Watermarking, Security, and Forensics, pp. 79–95 (2016)Google Scholar
  18. 18.
    Qian, Y., Dong, J., Wang, W., Tan, T.: Learning and transferring representations for image steganalysis using convolutional neural network. In: IEEE International Conference on Image Processing, pp. 2752–2756 (2016)Google Scholar
  19. 19.
    Qian, Y., Dong, J., Wang, W., Tan, T.: Learning representations for steganalysis from regularized CNN model with auxiliary tasks. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds.) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. LNEE, vol. 386, pp. 629–637. Springer, Heidelberg (2016). doi: 10.1007/978-3-662-49831-6_64 Google Scholar
  20. 20.
    Couchot, J.F., Couturier, R., Guyeux, C., Salomon, M.: Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key (2016)Google Scholar
  21. 21.
    Xu, G., Wu, H.Z., Shi, Y.Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)CrossRefGoogle Scholar
  22. 22.
    Xu, G., Wu, H.Z., Shi, Y.Q.: Ensemble of CNNs for steganalysis: an empirical study. In: ACM Workshop on Information Hiding and Multimedia Security, pp. 103–107 (2016)Google Scholar
  23. 23.
    Zeng, J., Tan, S., Li, B., Huang, J.: Large-scale JPEG steganalysis using hybrid deep-learning framework (2016)Google Scholar
  24. 24.
    Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Computer Science (2015)Google Scholar
  25. 25.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  26. 26.
    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). doi: 10.1007/3-540-36415-3_22 CrossRefGoogle Scholar
  27. 27.
    Kodovsky, J., Fridrich, J., Holub, V.: On dangers of overtraining steganography to incomplete cover model. In: ACM Multimedia and Security Workshop, pp. 69–76 (2011)Google Scholar
  28. 28.
    Fridrich, J., Kodovský, J., Holub, V., Goljan, M.: Breaking HUGO – the process discovery. In: Filler, T., Pevný, T., Craver, S., Ker, Andrew (eds.) IH 2011. LNCS, vol. 6958, pp. 85–101. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24178-9_7 CrossRefGoogle Scholar
  29. 29.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, MM 2014, pp. 675–678. ACM, New York (2014)Google Scholar
  30. 30.
    Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouChina
  2. 2.College of Information Science and TechnologyJinan UniversityGuangzhouChina
  3. 3.State Key Laboratory of Information SecurityInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina

Personalised recommendations