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Provably Secure Generative Steganography Based on Autoregressive Model

  • Kuan Yang
  • Kejiang Chen
  • Weiming ZhangEmail author
  • Nenghai Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11378)

Abstract

Synthetic data and generative models have been more and more popular with the rapid development of machine learning and artificial intelligence (AI). Consequently, generative steganography, a novel steganographic method finishing the operation of steganography directly in the process of image generation, tends to get more attention. However, most of the existing generative steganographic methods have more or less shortcomings, such as low security, small capacity or limited to certain images. In this paper, we propose a novel framework for generative steganography based on autoregressive model, or rather, PixelCNN. Theoretical derivation has been taken to prove the security of the framework. A simplified version is also proposed for binary embedding with lower complexity, for which the experiments show that the proposed method can resist the existing steganalytic methods.

Keywords

Steganography Provable security Steganalysis Generative model PixelCNN 

References

  1. 1.
    Filler, T., Fridrich, J.: Gibbs construction in steganography. IEEE Trans. Inf. Forensics Secur. 5(4), 705–720 (2010)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Holub, V., Fridrich, J.: Digital image steganography using universal distortion. In: Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp. 59–68. ACM (2013)Google Scholar
  4. 4.
    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
  5. 5.
    Sedighi, V., Cogranne, R., Fridrich, J.: Content-adaptive steganography by minimizing statistical detectability. IEEE Trans. Inf. Forensics Secur. 11(2), 221–234 (2016)CrossRefGoogle Scholar
  6. 6.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)CrossRefGoogle Scholar
  7. 7.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  8. 8.
    Denemark, T., Sedighi, V., Holub, V., Cogranne, R., Fridrich, J.: Selection-channel-aware rich model for steganalysis of digital images. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 48–53. IEEE (2014)Google Scholar
  9. 9.
    Guanshuo, X., Han-Zhou, W., Shi, Y.-Q.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)CrossRefGoogle Scholar
  10. 10.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
  11. 11.
    Goodfellow, I.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  12. 12.
    Larochelle, H., Murray, I.: The neural autoregressive distribution estimator. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 29–37 (2011)Google Scholar
  13. 13.
    Theis, L., Bethge, M.: Generative image modeling using spatial LSTMs. In: Advances in Neural Information Processing Systems, pp. 1927–1935 (2015)Google Scholar
  14. 14.
    van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016)
  15. 15.
    Hayes, J., Danezis, G.: Generating steganographic images via adversarial training. In: Advances in Neural Information Processing Systems, pp. 1951–1960 (2017)Google Scholar
  16. 16.
    Wu, K.-C., Wang, C.-M.: Steganography using reversible texture synthesis. IEEE Trans. Image Process. 24(1), 130–139 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Zhou, Z., Sun, H., Harit, R., Chen, X., Sun, X.: Coverless image steganography without embedding. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds.) ICCCS 2015. LNCS, vol. 9483, pp. 123–132. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27051-7_11CrossRefGoogle Scholar
  18. 18.
    van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with pixelCNN decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)Google Scholar
  19. 19.
    Salimans, T., Karpathy, A., Chen, X., Kingma, D.P.: PixelCNN++: improving the pixelCNN with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517 (2017)
  20. 20.
    Triantafyllidis, G.A., Strintzis, M.G.: A context based adaptive arithmetic coding technique for lossless image compression. IEEE Signal Process. Lett. 6(7), 168–170 (1999)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Hopper, N.J., Langford, J., von Ahn, L.: Provably secure steganography. In: Yung, M. (ed.) CRYPTO 2002. LNCS, vol. 2442, pp. 77–92. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45708-9_6CrossRefGoogle Scholar
  23. 23.
  24. 24.
  25. 25.
    Volkhonskiy, D., Borisenko, B., Burnaev, E.: Generative adversarial networks for image steganography (2016)Google Scholar
  26. 26.
    Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: Secure steganography based on generative adversarial networks. arXiv preprint arXiv:1707.01613 (2017)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kuan Yang
    • 1
  • Kejiang Chen
    • 1
  • Weiming Zhang
    • 1
    Email author
  • Nenghai Yu
    • 1
  1. 1.CAS Key Laboratory of Electromagnetic Space InformationUniversity of Science and Technology of ChinaHefeiChina

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