StegaFDS: Generative Steganography Based on First-Order DPM-Solver

Image steganography aims to conceal secret messages within an image without detection and has a long development history. With the rapid development of generative artificial intelligence, generative image steganography is also thriving. However, existing generative steganography methods struggle to balance steganographic capacity, extraction accuracy, and security. This paper proposes a high-capacity generative steganography method based on the first-order DPM-Solver, called StegaFDS. By utilizing our efficiently designed mapping function, which connects secret messages to the noise space of the diffusion probabilistic model (DPM), we achieve a significant increase in hiding capacity and detection resistance, while maintaining distribution-preserving. To improve message extraction accuracy further, we also optimize the existing first-order DPM-Solver inversion. Additionally, based on pre-trained diffusion models, StegaFDS can generate high-quality stego images without training. Experimental results show that StegaFDS performs exceptionally better than other generative steganography methods in the abovementioned metrics and demonstrates strong potential and availability.