  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.
