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Cross-Domain Image Steganography Based on GANs

  • Yaojie WangEmail author
  • Xiaoyuan Yang
  • Jia Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

According to the embedding method of secret information, steganography can be divided into: cover modification, selection and synthesis. In view of the problem that the cover modification will leave the modification trace, the cover selection is difficult and the load is too low, this paper proposes a cross-domain image steganography scheme based on GANs, which combines with cover synthesis. In the case that the cover type is not given in advance, the proposed scheme is driven by secret message, selects material to build the encrypted carrier, and converts it into cross-domain image, which is mapped to the generative image space for transmission. This scheme is consistent with the idea of coverless information hiding and can effectively resist the detection of steganalysis algorithm. Experiments were carried out on the data set of CelebA, and the results verified the feasibility and security of the scheme.

Keywords

Information hiding Cover synthesis Cross-domain Generative Adversarial Networks 

Notes

Acknowledgment

This work was supported by National Key R&D Program of China (Grant No. 2017YFB0802000), National Natural Science Foundation of China (Grant Nos. 61379152, 61403417)

References

  1. 1.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  2. 2.
    Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2010)zbMATHGoogle Scholar
  3. 3.
    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
  4. 4.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., WardeFarley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  5. 5.
    Zhou, Z.L., Cao, Y., Sun, X.M.: Coverless information hiding based on bag-of-words model of image. J. Appl. Sci. 34(5), 527–536 (2016)Google Scholar
  6. 6.
    Liu, M., Liu, J., Zhang, M., Li, T.: Tian generative information hiding method based on generative adversarial networks. J. Appl. Sci. 36(2), 27–36 (2018)Google Scholar
  7. 7.
    Otori, H., Kuriyama, S.: Data-embeddable texture synthesis. Smart graphics. In: Proceedings of the International Symposium, Sg 2007, Kyoto, Japan, June 25–27, 2007. DBLP, vol. 4569, pp. 146–157. (2007)Google Scholar
  8. 8.
    Otori, H., Kuriyama, S.: Texture synthesis for mobile data communications. IEEE Comput. Graphics Appl. 29(6), 74–81 (2009)CrossRefGoogle Scholar
  9. 9.
    Wu, K.C., Wang, C.M.: Steganography using reversible texture synthesis. IEEE Trans. Image Process. 24(1), 130 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Qian, Z., Zhou, H., Zhang, W., Zhang, X.: Robust steganography using texture synthesis. In: Processing of the Advances in Intelligent Information Hiding and Multimedia Signal. Springer International Publishing (2017)Google Scholar
  11. 11.
    Volkhonskiy, D., Nazarov, I., Borisenko, B., Burnaev, E.: Steganographic generative adversarial networks (2017) Google Scholar
  12. 12.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Comput. Sci.. (2015)Google Scholar
  13. 13.
    Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: Ssgan: secure steganography based on generative adversarial networks (2017)Google Scholar
  14. 14.
    Arjovsky, M., Bottou, L.: Towards Principled Methods for Training Generative Adversarial Networks [DB/OL]. [2017–2-09]. http://arxiv.org/abs/1701.04862
  15. 15.
    Tang, W., Tan, S., Li, B., Huang, J.: Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Process. Lett. 24(10), 1547–1551 (2017)CrossRefGoogle Scholar
  16. 16.
    Ke, Y., Zhang, M., Liu, J., Su, T., Yang, X.: Generative steganography with kerckhoffs’ principle based on generative adversarial networks (2017)Google Scholar
  17. 17.
    Liu, M., Liu, J., Zhang, M., Li, T.: Tian generative information hiding method based on generative adversarial networks. J. Appl. Sci. 36(2), 27–36 (2018)Google Scholar
  18. 18.
    Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier gans (2016)Google Scholar
  19. 19.
    Petitcolas, F.A.P.: Kerckhoffs’ Principle, 675–675 (2011)Google Scholar
  20. 20.
    Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks, 2242–2251 (2017)Google Scholar
  21. 21.
    Yi, Z., Zhang, H., Tan, P., et al.: DualGAN: unsupervised dual learning for image-to-image translation, 2868–2876 (2017)Google Scholar
  22. 22.
    Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation (2016)Google Scholar
  23. 23.
    Kim, T., Cha, M., Kim, H., et al.: Learning to discover cross-domain relations with generative adversarial networks. 한국지능정보시스템학회 학술대회논문집 (2017)Google Scholar
  24. 24.
    Isola, P., Zhu, J.Y., Zhou, T., et al.: Image-to-image translation with conditional adversarial networks, 5967–5976 (2016)Google Scholar
  25. 25.
    Dumoulin, V., Belghazi, I., Poole, B., et al.: Adversarially Learned Inference (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Engineering University of PAPXi’anChina
  2. 2.Key Laboratory of Network and Information Security of PAPXi’anChina

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