GAN-Based Steganography with the Concatenation of Multiple Feature Maps

  • Haibin Wu
  • Fengyong LiEmail author
  • Xinpeng Zhang
  • Kui Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12022)


Steganography has been widely used to conceal secret information in multimedia content. Using generative adversarial networks (GAN) where two subnetworks compete against each other, steganography can learn good distortion measurement. Nevertheless, the convergence speed of GAN is usually slow, and the performance of GAN-based steganography has large room to improve. In this paper, we propose a new GAN-based spatial steganographic scheme. The proposed learning framework consists of two parts: a steganographic generator and a steganalytic discriminator. The former generates stego images, and the latter evaluates their steganography security. Different from existing GAN-based steganography, we reconstruct the generator by combining multiple feature maps, and then expand the maximum number of feature channels to 256. The reconstruction generator can effectively generate a sophisticated probability map, which is used to calculate optimal distortion measurement and further provides a better guidance for adaptive information embedding. Comprehensive experimental results show that, with the same discriminant network, the anti-steganalysis performance of our method is better than that of ASDL-GAN scheme and Yang’s scheme.


Steganography Generative adversarial networks Multiple feature maps Content-adaptive 



This work was supported by Natural Science Foundation of China under Grants (61602295, U1736120), the Foreign Visiting Scholar Program of Shanghai Municipal Education Commission and Postgraduate Innovation and Entrepreneurship Project of Shanghai University of Electric Power (A-0201-19-183Y-20).


  1. 1.
    Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)CrossRefGoogle Scholar
  2. 2.
    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). Scholar
  3. 3.
    Fridrich, J., Holub, V.: Designing steganographic distortion using directional filters. In: IEEE International Workshop on Information Forensics and Security, WIFS 2012, pp. 234–239. IEEE (2012).
  4. 4.
    Li, B., Tan, S., Wang, M., Huang, J.: Investigation on cost assignment in spatial image steganography. IEEE Trans. Inf. Forensics Secur. 9(8), 1264–1277 (2014)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), 1–13 (2014). Scholar
  6. 6.
    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
  7. 7.
    Xu, G., Wu, H., Shi, Y.: Structural design of convolutional neural networks for steganalysis. IEEE Signal Process. Lett. 23(5), 708–712 (2016)CrossRefGoogle Scholar
  8. 8.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Yang, J., Liu, K., Kang, X., Wong, E.K.: Spatial image steganography based on generative adversarial network. arXiv:1804.07939 (2018)
  11. 11.
    Li, F., Wu, K., Zhang, X., Yu, J., Lei, J., Wen, M.: Robust batch steganography in social networks with non-uniform payload and data decomposition. IEEE Access 6, 29912–29925 (2018)CrossRefGoogle Scholar
  12. 12.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2016)
  13. 13.
    Shi, H., Dong, J., Wang, W., Qian, Y., Zhang, X.: SSGAN: secure steganography based on generative adversarial networks. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds.) PCM 2017. LNCS, vol. 10735, pp. 534–544. Springer, Cham (2018). Scholar
  14. 14.
    Li, F., Zhang, X., Cheng, H., Jiang, Y.: Digital image steganalysis based on local textural features and double dimensionality reduction. Secur. Commun. Netw. 9(8), 729–736 (2016)CrossRefGoogle Scholar
  15. 15.
    Volkhonskiy, D., Nazarov, I., Borisenko, B., Burnaev, E.: Steganographic generative adversarial networks. arXiv:1703.05502 (2017)
  16. 16.
    Li, F., Wu, K., Lei, J., Wen, M., Bi, Z., Gu, C.: Steganalysis over large-scale social networks with high-order joint features and clustering ensembles. IEEE Trans. Inf. Forensics Secur. 11(2), 344–357 (2016)CrossRefGoogle Scholar
  17. 17.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 (2017)
  18. 18.
    Places365-Standard (2019). Accessed Mar 2019
  19. 19.
    DDE Download (2019). Accessed Mar 2019
  20. 20.
    Qian, Y., Dong, J., Wang W., Tan, T.: Deep learning for steganalysis via convolutional neural networks. In: Media Watermarking, Security, and Forensics 2015, Proceedings of SPIE, vol. 9409 (2015).
  21. 21.
    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
  22. 22.
    Zhang Y., Zhang W., Chen K., et al.: Adversarial examples against deep neural network based steganalysis. In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 67–72. ACM (2018)Google Scholar
  23. 23.
    Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3), 920–935 (2011)CrossRefGoogle Scholar
  24. 24.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Haibin Wu
    • 1
  • Fengyong Li
    • 1
    • 3
    Email author
  • Xinpeng Zhang
    • 2
  • Kui Wu
    • 3
  1. 1.College of Computer Science and TechnologyShanghai University of Electric PowerShanghaiPeople’s Republic of China
  2. 2.School of Computer ScienceFudan UniversityShanghaiPeople’s Republic of China
  3. 3.Computer Science DepartmentUniversity of VictoriaVictoriaCanada

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