Training-Free Coverless Multi-Image Steganography with Access Control

2026-03-10

AI Summary

This work addresses the challenge of enabling differentiated information access in multi-user scenarios within existing carrier-free image steganography methods. To this end, we propose MIDAS, a novel framework that, for the first time, achieves training-free diffusion-based multi-user carrier-free steganography. MIDAS embeds user-specific access control policies during the diffusion process by leveraging latent vector fusion and a random basis mechanism, effectively suppressing residual structural artifacts. Experimental results demonstrate that MIDAS significantly outperforms current training-free baselines in terms of access control capability, stego-image quality and diversity, robustness to noise, and resistance against steganalytic detection.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance

Abstract

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, 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 and a Latent Vector Fusion module that reshapes aggregated latents to 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.

Problem

• Coverless Image Steganography
• Access Control
• Multi-Image Hiding
• Steganalysis Resistance
• Latent Fusion

Innovation

• Coverless Steganography
• Access Control
• Diffusion Models
• Latent Fusion
• Training-Free