The paper introduces a well-posed invertible 1x1 convolution that eliminates random sampling in reversible image conversion, achieving state-of-the-art results on multiple tasks.
Diffusion-Based Hierarchical Image Steganography
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abstract
This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models. HIS assigns varying levels of robustness to images based on their importance, ensuring enhanced protection against manipulation. It adaptively exploits the robustness of the Diffusion Model alongside the reversibility of the Flow Model. The integration of Embed-Flow and Enhance-Flow improves embedding efficiency and image recovery quality, respectively, setting HIS apart from conventional multi-image steganography techniques. This innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text. Rigorous subjective and objective evaluations underscore our advantage in analytical resistance, robustness, and capacity, illustrating its expansive applicability in content safeguarding and privacy fortification.
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eess.IV 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Tackling Ill-posedness of Reversible Image Conversion with Well-posed Invertible Network
The paper introduces a well-posed invertible 1x1 convolution that eliminates random sampling in reversible image conversion, achieving state-of-the-art results on multiple tasks.