Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achieve translation between two images without training, and robustness to noisy data, can be used to improve security and natural robustness in image steganography tasks. For the choice of diffusion model, we selected Stable Diffusion, a type of conditional diffusion model, and fully utilized the latest tools from open-source communities, such as LoRAs and ControlNets, to improve the controllability and diversity of container images. In summary, we propose a novel image steganography framework, named Controllable, Robust and Secure Image Steganography (CRoSS), which has significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. These benefits are obtained without additional training. To our knowledge, this is the first work to introduce diffusion models to the field of image steganography. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed CRoSS framework in controllability, robustness, and security.

翻译：当前图像隐写技术主要聚焦于基于封面图像的方法，这类方法普遍存在秘密图像泄露风险，且对退化后的容器图像鲁棒性较差。受扩散模型最新进展启发，我们发现扩散模型的两个特性——无需训练即可实现两幅图像间的转换能力，以及对噪声数据的鲁棒性——可被用于提升图像隐写任务的安全性与天然鲁棒性。在扩散模型选择上，我们采用了条件扩散模型Stable Diffusion，并充分利用开源社区的最新工具（如LoRAs和ControlNets）提升容器图像的可控性与多样性。综上，我们提出一种名为可控、鲁棒且安全图像隐写（CRoSS）的新型图像隐写框架，该框架在可控性、鲁棒性和安全性方面相较于基于封面图像的方法具有显著优势，且这些优势无需额外训练即可获得。据我们所知，这是首个将扩散模型引入图像隐写领域的研究工作。在实验部分，我们通过详细实验证明了所提CRoSS框架在可控性、鲁棒性和安全性方面的优势。