GTSD: Generative Text Steganography Based on Diffusion Model

Abstract: With the rapid development of deep learning, existing generative text steganography methods based on autoregressive models have achieved success. However, these autoregressive steganography approaches have certain limitations. Firstly, existing methods require encoding candidate words according to their output probability and generating each stego word one by one, which makes the generation process time-consuming. Secondly, encoding and selecting candidate words changes the sampling probabilities, resulting in poor imperceptibility of the stego text. Thirdly, existing methods have low robustness and cannot resist replacement attacks. To address these issues, we propose a generative text steganography method based on a diffusion model (GTSD), which improves generative speed, robustness, and imperceptibility while maintaining security. To be specific, a novel steganography scheme based on diffusion model is proposed to embed secret information through prompt mapping and batch mapping. The prompt mapping maps secret information into a conditional prompt to guide the pre-trained diffusion model generating batches of candidate sentences. The batch mapping selects stego text based on secret information from batches of candidate sentences. Extensive experiments show that the GTSD outperforms the SOTA method in terms of generative speed, robustness, and imperceptibility while maintaining comparable anti-steganalysis performance. Moreover, we verify that the GTSD has strong potential: embedding capacity is positively correlated with prompt capacity and model batch sizes while maintaining security.