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Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13805–13818 | Cite as

Generative steganography with Kerckhoffs’ principle

  • Yan KeEmail author
  • Min-qing Zhang
  • Jia Liu
  • Ting-ting Su
  • Xiao-yuan Yang
Article
  • 117 Downloads

Abstract

The distortion in steganography that usually comes from the modification or recoding of the cover image during the embedding process. And it is the embedding distortion that leaves the steganalyzer with possible discrimination. Therefore, we propose generative steganography with Kerckhoffs’ principle (GSK) in this paper. In GSK, the secret messages are generated by a cover image using a generator rather than embedded into the cover, which results in no modifications to the cover. To ensure security, the generators are trained to meet Kerckhoffs’ principle based on generative adversarial networks (GANs). Everything about the GSK system is public knowledge for the receivers, except the extraction key. The secret messages can be outputted by the generator if and only if the extraction key and the cover image are both inputted. In the generator training procedures, there are two GANs (Message-GAN and Cover-GAN) that are designed to work jointly, making the generated results under the control of the extraction key and the cover image. We provide experimental results for the training process. We present an example of the working process by adopting a generator trained on the dataset MNIST, which demonstrates that GSK can use a cover image without any modification to generate messages. Furthermore, only meaningless results would be obtained without the extraction key or the cover image.

Keywords

Information security Generative steganography Generative adversarial networks (GANs) Kerckhoffs’ principle 

Notes

Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant 2017YFB0802000.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yan Ke
    • 1
    Email author
  • Min-qing Zhang
    • 1
  • Jia Liu
    • 1
  • Ting-ting Su
    • 1
  • Xiao-yuan Yang
    • 1
  1. 1.Key Laboratory of Network and Information Security Under the Chinese People Armed Police Force (PAP)Engineering University of PAPXi’anChina

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