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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 32503–32522 | Cite as

Adaptive spatial steganography based on adversarial examples

  • Sai Ma
  • Xianfeng ZhaoEmail author
  • Yaqi Liu
Article
  • 100 Downloads

Abstract

Recently, the researchers start to apply adversarial attack to enhance the security of steganographic algorithms. The typical deep learning model is vulnerable to adversarial attack. Such attack is generating special instance via neural network. The generated instance can increase the detection error of the steganalyzer. In this paper, we propose a practical adversarial method to enhance the security of typical distortion-minimizing steganographic algorithms. The proposed method is an adaptation of the Fast Gradient Sign Method in the steganography. We utilize the gradients back-propagated from the deep-learning steganalyzer to control the changing direction of the pixels. This kind of steganaographic modification in the image helps to improve the security towards the steganalysis. The experimental results prove that the proposed method can enhance the security of typical distortion-minimizing steganaographic algorithms.

Keywords

Steganography Adversarial attack Deep learning Steganalysis 

Notes

Acknowledgments

The authors would like to thank the members of DDE Laboratory in SUNY Binghamton for sharing their codes and image library, and the members of MICS Laboratory in Shenzhen University for sharing their codes. The authors would also like to thank the authors of Tensorflow and Keras.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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