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Hiding Message Using a Cycle Generative Adversarial Network

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Published:01 November 2022Publication History
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Abstract

Training an image steganography is an unsupervised problem, because it is impossible to obtain an ideal supervised steganographic image corresponding to the cover image and secret message. Inspired by the success of cycle generative adversarial networks in unsupervised tasks such as style transfer, this article proposes to use a cycle generative adversarial network to solve the problem of unsupervised image steganography. Specifically, this article jointly trains five networks, i.e., a steganographic network, an inverse steganographic network, a hidden message reconstruction network, and two discriminative networks, which together constitute a hidden message cycle generative adversarial network (HCGAN). Compared with the recent image steganography based on generative adversative network, HCGAN provides more accurate supervised information, which makes the training process of HCGAN converge faster and the performance of the trained image steganography network is better. In addition, this article introduces an image steganographic network based on residual learning and shows that residual learning can effectively improve the performance of steganography. Furthermore, to the best of our knowledge, we are the first to propose an inverse steganographic network for eliminating steganographic message from steganographic images, which can be used to avoid steganographic message being discovered or acquired by a third party. The experimental results show that compared with the steganography based on generative adversarial network, the proposed HCGAN has a higher correct decoding rate, better visual quality of steganographic image, and higher secrecy.

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        • Published in

          cover image ACM Transactions on Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3s
          October 2022
          381 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3567476
          • Editor:
          • Abdulmotaleb El Saddik
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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          Publication History

          • Published: 1 November 2022
          • Online AM: 12 March 2022
          • Accepted: 3 November 2021
          • Revised: 18 August 2021
          • Received: 30 January 2021
          Published in tomm Volume 18, Issue 3s

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