CoAS: Composite Audio Steganography Based on Text and Speech Synthesis
Yiming Li, Kejiang Chen, Yaofei Wang, Xin Zhang, Guanjie Wang, Weiming Zhang, Nenghai Yu · IEEE Transactions on Information Forensics and Security · 2025
Digital steganography is the practice of embedding secret information in original normal data to enable covert communication. With the rapid advancement of generative models, generative steganography has gained renewed vitality. As a key medium on the Internet, audio has also become a focus of steganographic research. However, existing audio steganography methods rely on traditional audio synthesis models, which often suffer from suboptimal synthesis quality. In contrast, diffusion models perform well in audio synthesis tasks, but there is a lack of targeted secure audio steganography methods based on them. In addition, existing steganography schemes are generally limited to transmitting only the steganographic object, and other key elements need to be negotiated in advance, which limits their practicality. To address these issues, we propose CoAS, a composite audio steganography method based on text and speech synthesis. Firstly, we use a provably secure linguistic steganography method to embed the synchronous side information required for audio steganography, and then replace the gaussian noise in the diffusion models with message-driven sampling during the audio generation process. Both theoretical analysis and experimental results validate the security and practicality of our composite steganography method in the real world. Audio samples are available at https://meterial.github.io/coas.github.io.