Diffusion-Based Hierarchical Image Steganography

TL;DR

This paper presents Hierarchical Image Steganography (HIS), a diffusion model-based method that securely embeds multiple images and text into a single container with enhanced robustness, capacity, and adaptive protection.

Contribution

It introduces a novel hierarchical steganography framework utilizing diffusion and flow models for improved security, capacity, and autonomous container generation.

Findings

HIS outperforms traditional methods in robustness and capacity.

The method effectively conceals multiple images and text.

Evaluations show superior resistance and image recovery quality.

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.