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Camouflage generative adversarial network: a coverless full-image-to-image hiding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Xiyao Liu
  • Ziping Ma
  • Xingbei Guo
  • Jialu Hou
  • Gerald Schaefer
  • Lei Wang
  • Dr Victoria Wang
  • Hui Fang
Image hiding, one of the most important data hiding techniques, is widely used to enhance cybersecurity when transmitting multimedia data. In recent years, deep learning- based image hiding algorithms have been designed to improve the embedding capacity whilst maintaining sufficient imperceptibility to malicious eavesdroppers. These methods can hide a full-size secret image into a cover image, thus allowing full-image-to- image hiding. However, these methods suffer from a trade- off challenge to balance the possibility of detection from the container image against the recovery quality of secret image. In this paper, we propose Camouflage Generative Adversarial Network (Cam-GAN), a novel two-stage coverless full-image- to-image hiding method named, to tackle this problem. Our method offers a hiding solution through image synthesis to avoid using a modified cover image as the image hiding container and thus enhancing both image hiding imperceptibility and recovery quality of secret images. Our experimental results demonstrate that Cam-GAN outperforms state-of-the-art full-image-to-image hiding algorithms on both aspects.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE International Conference on Systems, Man and Cybernetics
Publication statusAccepted for publication - 21 Aug 2020
Event2020 IEEE International Conference on Systems, Man and Cybernetics: SMC 2020 - Toronto, Canada
Duration: 11 Oct 202014 Oct 2020


Conference2020 IEEE International Conference on Systems, Man and Cybernetics
Internet address


  • SMC20_0881_FI

    Rights statement: The embargo end date of 2050 is a temporary measure until we know the publication date. Once we know the publication date the full text of this article will be able to view shortly afterwards.

    Accepted author manuscript (Post-print), 3.43 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

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