  Traditional image steganography focuses on concealing one image within
another, aiming to avoid steganalysis by unauthorized entities. Coverless image
steganography (CIS) enhances imperceptibility by not using any cover image.
Recent works have utilized text prompts as keys in CIS through diffusion
models. However, this approach faces three challenges: invalidated when private
prompt is guessed, crafting public prompts for semantic diversity, and the risk
of prompt leakage during frequent transmission. To address these issues, we
propose DiffStega, an innovative training-free diffusion-based CIS strategy for
universal application. DiffStega uses a password-dependent reference image as
an image prompt alongside the text, ensuring that only authorized parties can
retrieve the hidden information. Furthermore, we develop Noise Flip technique
to further secure the steganography against unauthorized decryption. To
comprehensively assess our method across general CIS tasks, we create a dataset
comprising various image steganography instances. Experiments indicate
substantial improvements in our method over existing ones, particularly in
aspects of versatility, password sensitivity, and recovery quality. Codes are
available at \url{https://github.com/evtricks/DiffStega}.
