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GAN-Based Steganography with the Concatenation of Multiple Feature Maps

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

Abstract

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.

Keywords

Steganography Generative adversarial networks Multiple feature maps Content-adaptive 

Notes

Acknowledgments

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).

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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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