无需训练的多图隐写系统，支持用户分级访问控制。
Training-Free Coverless Multi-Image Steganography with Access Control
- 基于扩散模型的潜在空间融合，实现无损隐写。
- 可区分不同用户访问不同隐藏内容，且图像质量高。
- 适合需要隐私保护的多用户信息隐藏场景。
无载体图像隐写（CIS）通过不修改载体图像来隐藏信息，具备强不可察觉性和对隐写分析的天然鲁棒性。然而现有CIS方法普遍缺乏稳健的访问控制机制，难以在多用户环境下为不同授权用户选择性揭示不同隐藏内容。为此，本文提出MIDAS（多图扩散式访问控制隐写），一种无需训练的扩散模型驱动的CIS框架，通过潜在空间融合实现多图像隐写与用户级访问控制。MIDAS引入随机基机制抑制残留结构信息，并提供信息泄露的理论分析；其潜在向量融合模块可重塑聚合潜在变量，使其更契合扩散过程。实验表明，MIDAS在访问控制功能、隐写图像质量与多样性、抗噪声能力及抗隐写分析方面，均持续优于现有无训练基线方法，构建了实用且可扩展的访问控制无载体隐写方案。
原文摘要 · Abstract (English)
Coverless Image Steganography (CIS) hides information without explicitly modifying a cover image, providing strong imperceptibility and inherent robustness to steganalysis. However, existing CIS methods largely lack robust access control, making it difficult to selectively reveal different hidden contents to different authorized users. Such access control is critical for scalable and privacy-sensitive information hiding in multi-user settings. We propose MIDAS (Multi-Image Diffusion-based Access-controlled Steganography), a training-free diffusion-based CIS framework that enables multi-image hiding with user-specific access control via latent-level fusion. MIDAS introduces a Random Basis mechanism to suppress residual structural information, together with a theoretical analysis of information leakage, and a Latent Vector Fusion module that reshapes aggregated latents to better align with the diffusion process. Experimental results demonstrate that MIDAS consistently outperforms existing training-free CIS baselines in access control functionality, stego image quality and diversity, robustness to noise, and resistance to steganalysis, establishing a practical and scalable approach to access-controlled coverless steganography.
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