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Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning

  • Haichao Shi
  • Xiao-Yu Zhang
  • Shupeng WangEmail author
  • Ge FuEmail author
  • Jianqi Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

In this work, we mainly study the mechanism of learning the steganographic algorithm as well as combining the learning process with adversarial learning to learn a good steganographic algorithm. To handle the problem of embedding secret messages into the specific medium, we design a novel adversarial module to learn the steganographic algorithm, and simultaneously train three modules called generator, discriminator and steganalyzer. Different from existing methods, the three modules are formalized as a game to communicate with each other. In the game, the generator and discriminator attempt to communicate with each other using secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. We show that through unsupervised adversarial training, the adversarial model can produce robust steganographic solutions, which acts like an encryption. Furthermore, we propose to utilize supervised adversarial training method to train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Extensive experiments demonstrate the effectiveness of the proposed method on publicly available datasets.

Keywords

Steganography Steganalysis Adversarial learning 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant 61871378), and the Open Project Program of National Laboratory of Pattern Recognition (Grant 201800018).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.National Computer Network Emergency Response Technical Team/Coordination Center of ChinaBeijingChina

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