  Generative steganography (GS) is an emerging technique that generates stego
images directly from secret data. Various GS methods based on GANs or Flow have
been developed recently. However, existing GAN-based GS methods cannot
completely recover the hidden secret data due to the lack of network
invertibility, while Flow-based methods produce poor image quality due to the
stringent reversibility restriction in each module. To address this issue, we
propose a novel GS scheme called "Generative Steganography Diffusion" (GSD) by
devising an invertible diffusion model named "StegoDiffusion". It not only
generates realistic stego images but also allows for 100\% recovery of the
hidden secret data. The proposed StegoDiffusion model leverages a non-Markov
chain with a fast sampling technique to achieve efficient stego image
generation. By constructing an ordinary differential equation (ODE) based on
the transition probability of the generation process in StegoDiffusion, secret
data and stego images can be converted to each other through the approximate
solver of ODE -- Euler iteration formula, enabling the use of irreversible but
more expressive network structures to achieve model invertibility. Our proposed
GSD has the advantages of both reversibility and high performance,
significantly outperforming existing GS methods in all metrics.
