Generative Information Hiding Method Based on Adversarial Networks

  • Zhuo ZhangEmail author
  • Guangyuan Fu
  • Jia Liu
  • Wenyu Fu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


Traditional Steganography need to modify the carrier image to hide information, which will leave traces of rewriting, then eventually be perceived by the enemy. In this paper, an information hiding scheme based on Auxiliary Classifier Generative Adversarial Networks (AC-GANs) model is proposed for Steganography. This method designs and trains the networks model based on AC-GANs by constructing a dedicated dictionary and image database. The sender can map the secret information into the category labels through the dictionary, and then use the labels generate the real looking images to be sent through the model. On the contrary, the receiver can identify the image label through the model and obtain the secret information. Through experiments, the feasibility of this method is verified and the reliability of the algorithm is analyzed. This method transmits secret messages by generating images without overwriting the carrier images. It can effectively solve the problem of modification of carrier images in traditional information hiding.


Steganography Information hiding AC-GANs GAN 


  1. 1.
    Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: Digital image steganography: Survey and analysis of current methods. Sig. Process. 90(3), 727–752 (2010)CrossRefGoogle Scholar
  2. 2.
    Wu, H.C., Wu, N., Tsai, C.S., Hwang, M.S.: Image steganographic scheme based onpixel-value differencing and LSB replacement methods. Proc. Vis. Image Signal Process. 152(5), 611–615 (2005)CrossRefGoogle Scholar
  3. 3.
    Mielikainen, J.: LSB matching revisited. IEEE Signal Process. Lett. 13(5), 285–287 (2006)CrossRefGoogle Scholar
  4. 4.
    Yang, C.H., Weng, C.Y., Wang, S.J.: Adaptive data hiding in edge areas of images with spatial LSB domain systems. IEEE Trans. Inf. Forensics Secur. 3(3), 488–497 (2008)CrossRefGoogle Scholar
  5. 5.
    Cox, I.J., Kilian, J., Leighton, F.T.: Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. 6(12), 1673–1687 (2010)CrossRefGoogle Scholar
  6. 6.
    Ruanaidh, J.J.K.O., Dowling, W.J., Boland, F.M.: Phase watermarking of digital images. In: International Conference on Image Processing, pp. 239–242 (1996)Google Scholar
  7. 7.
    Sun, Q.D., Guan, P., Qiu, Y., Yan, W.Y.: DWT domain information hiding approach using detail sub-band feature adjustment. Telkomnika Indones. J. Electr. Eng. 11(7) (2013)Google Scholar
  8. 8.
    Zhou, Z.L., Sun, H.Y., Harit, R.: Coverless image steganography without embedding. In: ICCCS 2015. LNCS, vol. 9483, pp. 123–132 (2015)CrossRefGoogle Scholar
  9. 9.
    Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, Cambridge (2010)zbMATHGoogle Scholar
  10. 10.
    Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR, vol. 70 (2017)Google Scholar
  11. 11.
    Ian, G., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680 (2014)Google Scholar
  12. 12.
    The MNIST Database of Handwritten Digits.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Xi’an High-Tech InstituteXi’anChina
  2. 2.Key Lab of Networks and Information Security of PAPXi’anChina
  3. 3.China Huadian Corporation LTD Sichuan Baozhusi Hydropower PlantGuangyuanChina

Personalised recommendations