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.

翻译：生成式隐写（GS）是一种新兴技术，可直接从秘密数据生成隐写图像。近年来，基于GAN或Flow的各类GS方法已得到发展。然而，现有基于GAN的GS方法因缺乏网络可逆性而无法完全恢复隐藏的秘密数据，而基于Flow的方法因每个模块中严格的逆可逆性限制导致图像质量较差。为解决此问题，我们提出了一种名为"生成式隐写扩散"（GSD）的新型GS方案，通过设计名为"StegoDiffusion"的可逆扩散模型实现。该模型不仅能生成逼真的隐写图像，还可实现隐藏秘密数据100%的恢复。所提出的StegoDiffusion模型利用非马尔可夫链与快速采样技术实现高效隐写图像生成。通过基于StegoDiffusion生成过程的转移概率构建常微分方程（ODE），秘密数据与隐写图像可通过ODE的近似求解器——欧拉迭代公式相互转换，从而允许使用不可逆但表达能力更强的网络结构实现模型可逆性。我们提出的GSD兼具可逆性与高性能优势，在所有评估指标上显著优于现有GS方法。