Large-capacity image steganography based on invertible neural networks

Lu, Shao-Ping, Wang, Rong, Zhong, Tao and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2021. Large-capacity image steganography based on invertible neural networks. Presented at: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20-25 June 2021. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 10811-10820. 10.1109/CVPR46437.2021.01067

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Abstract

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-6654-4509-2
Date of First Compliant Deposit:	29 October 2026
Last Modified:	29 Oct 2026 12:31
URI:	https://orca.cardiff.ac.uk/id/eprint/190521

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