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Camouflage generative adversarial network: Coverless full-image-to-image hiding

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conference contribution
posted on 17.12.2020, 10:59 by Xiyao Liu, Ziping Ma, Xingbei Guo, Jialu Hou, Gerald SchaeferGerald Schaefer, Lei Wang, Victoria Wang, Hui FangHui 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.

Funding

National Natural Science Foundation of China (61602527)

Natural Science Foundation of Hunan Province, China (2020JJ4746, 2017JJ3416, 2018JJ2548)

History

School

  • Science

Department

  • Computer Science

Published in

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Pages

166-172

Source

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-12-14

ISBN

9781728185262

ISSN

2577-1655

Language

en

Location

TORONTO, CANADA

Event dates

11th October 2020 - 14th October 2020

Depositor

Dr Hui Fang. Deposit date: 16 December 2020