Improved Generative Steganography Based on Diffusion Model

The rapid growth of generative models has led to a new direction in steganography called generative steganography (GS). It allows message-to-image generation without the need for a carrier image. Recently, generative steganography methods have been proposed using generative adversarial networks (GANs) and Flow models. On the one hand, methods that use GANs to generate stego images struggle to fully recover the hidden message because the networks are not reversible. On the other hand, methods based on Flow encounter a problem where the images they create might not look real, mainly because the network has limitations in being reversible. Diffusion models fulfill network reversibility while generating high-quality images. However, the framework of existing diffusion models is reversible, but hidden message recovery is not perfectly reversible, resulting in the recovered message being similar but not exactly the same as the hidden one. Existing diffusion models are typically trained for one-directional image generation tasks, so they face some problems when dealing with bi-directional steganography tasks. If pre-trained diffusion models are directly used to generate stego images, exact secret data extraction through the diffusion process cannot be achieved. In this paper, we present an improved generative steganography based on the diffusion model (GSD), which conceals secret data in the frequency domain of random noise to enhance the security and accuracy of steganography, and re-trains the denoising diffusion implicit model (DDIM) for steganography, called the StegoDiffusion. During training StegoDiffusion, random noise is injected into the clean natural images and then trained through the forward diffusion process to obtain the re-trained StegoDiffusion. Our proposed GSD scheme achieves a 100% extraction accuracy for hidden secret data with a payload of 1 bit-per-pixel (bpp) in a single channel, and generates high-quality stego images in PNG format.