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A New Steganalysis Method Using Densely Connected ConvNets

  • Brijesh SinghEmail author
  • Prasen Kumar Sharma
  • Rupal Saxena
  • Arijit Sur
  • Pinaki Mitra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)

Abstract

Steganography is an ancient art of communicating a secret message through an innocent-looking image. On the other hand, steganalysis is the counter process of the steganography, which targets to detect hidden trace within a given image. In this paper, a new approach to steganalysis is presented to learn prominent features and avoid loss of stego signals. The proposed model uses diverse sized filters to capture all useful steganalytic features through a densely connected convolutional network. Moreover, there is no fully connected network in the proposed model, which allows testing any size of images regardless of the image size used for training. To justify the applicability of the proposed scheme, it has been shown experimentally that the proposed scheme outperforms most of the related state-of-the-art methods.

Keywords

Steganography Steganalysis Convolutional Neural Network DenseNet 

References

  1. 1.
    Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90–93 (1974)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bas, P., Filler, T., Pevný, T.: “Break Our Steganographic System”: the ins and outs of organizing BOSS. In: Filler, T., Pevný, T., Craver, S., Ker, A. (eds.) IH 2011. LNCS, vol. 6958, pp. 59–70. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24178-9_5CrossRefGoogle Scholar
  3. 3.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  4. 4.
    Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Their Appl. 13(4), 18–28 (1998)CrossRefGoogle Scholar
  5. 5.
    Holub, V., Fridrich, J.: Designing steganographic distortion using directional filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 234–239. IEEE (2012)Google Scholar
  6. 6.
    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
  7. 7.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)Google Scholar
  8. 8.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  9. 9.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  10. 10.
    Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2011)CrossRefGoogle Scholar
  11. 11.
    Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 4206–4210. IEEE (2014)Google Scholar
  12. 12.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
  13. 13.
    Paszke, A., Gross, S., Chintala, S., Chanan, G.: Pytorch: tensors and dynamic neural networks in Python with strong GPU acceleration. PyTorch: tensors and dynamic neural networks in Python with strong GPU acceleration 6 (2017)Google Scholar
  14. 14.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)CrossRefGoogle Scholar
  15. 15.
    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).  https://doi.org/10.1007/978-3-642-16435-4_13CrossRefGoogle Scholar
  16. 16.
    Qian, Y., Dong, J., Wang, W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Media Watermarking, Security, and Forensics 2015, vol. 9409, p. 94090J. International Society for Optics and Photonics (2015)Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  18. 18.
    Tian, J., Li, Y.: Convolutional neural networks for steganalysis via transfer learning. Int. J. Pattern Recognit Artif Intell. 33(02), 1959006 (2019)CrossRefGoogle Scholar
  19. 19.
    Tijms, H.C., Tijms, H.C.: Stochastic Models: An Algorithmic Approach, vol. 994. Wiley, Chichester (1994)zbMATHGoogle Scholar
  20. 20.
    Wu, H.C., Wu, N.I., Tsai, C.S., Hwang, M.S.: Image steganographic scheme based on pixel-value differencing and LSB replacement methods. IEEE Proc.-Vis. Image Signal Process. 152(5), 611–615 (2005)CrossRefGoogle 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.
    Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of ChemistryIndian Institute of Technology GuwahatiGuwahatiIndia

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