Efficient and Separate Authentication Image Steganography Network
Junchao Zhou, Yao Lu, Jie Wen, Guangming Lu
Abstract
Image steganography hides multiple images for multiple recipients into a single cover image. All secret images are usually revealed without authentication, which reduces security among multiple recipients. It is elegant to design an authentication mechanism for isolated reception. We explore such mechanism through sufficient experiments, and uncover that additional authentication information will affect the distribution of hidden information and occupy more hiding space of the cover image. This severely decreases effectiveness and efficiency in large-capacity hiding. To overcome such a challenge, we first prove the authentication feasibility within image steganography. Then, this paper proposes an image steganography network collaborating with separate authentication and efficient scheme. Specifically, multiple pairs of lock-key are generated during hiding and revealing. Unlike traditional methods, our method has two stages to make appropriate distribution adaptation between locks and secret images, simultaneously extracting more reasonable primary information from secret images, which can release hiding space of the cover image to some extent. Furthermore, due to separate authentication, fused information can be hidden in parallel with a single network rather than traditional serial hiding with multiple networks, which can largely decrease the model size. Extensive experiments demonstrate that the proposed method achieves more secure, effective, and efficient image steganography. Code is available at https://github.com/Revive624/Authentication-Image-Steganography .
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Cited by top-tier papers2
- Training-Free Coverless Multi-Image Steganography with Access ControlMinyeol Bae, Si-Hyeon LeeICML 2026
- Large-capacity and Receiver Authenticable Generative Image SteganographyJiannian Wang, Yao Lu, Guangming LuICML 2026
Builds on9
- HiNet: Deep Image Hiding by Invertible NetworkJunpeng Jing, Xin Deng, Mai Xu, Jianyi Wang et al.ICCV 2021 · 301 citations
- Robust Invertible Image SteganographyYoumin Xu, Chong Mou, Yujie Hu, Jingfen Xie et al.CVPR 2022 · 151 citations
- Fixed Neural Network Steganography: Train the images, not the networkVarsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li et al.ICLR 2022 · 59 citations
- Representational aspects of depth and conditioning in normalizing flowsFrederic Koehler, Viraj Mehta, Andrej RisteskiICML 2021 · 29 citations
- Hiding Images in Deep Probabilistic ModelsHaoyu Chen, Linqi Song, Zhenxing Qian, Xinpeng Zhang et al.NeurIPS 2022 · 20 citations
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