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.

通过使用扩散模型来增强嵌入多个图像到单个容器的安全性和容量，本研究介绍了层次图像隐写术。层次图像隐写术根据图像的重要性分配不同程度的鲁棒性，以增强对篡改的保护。它灵活利用了扩散模型的鲁棒性和流模型的可逆性。通过集成Embed-Flow和Enhance-Flow，提高了嵌入效率和图像恢复质量，使其与传统的多图像隐写术技术有所区别。这种创新的结构能够自动生成容器图像，从而安全高效地隐藏多个图像和文本。经过严格的主观和客观评估，我们的优势在于分析抗性、鲁棒性和容量，显示出其在内容保护和隐私加固方面的广泛适用性。

基于扩散的分层图像隐写术