  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.
