Coverless Image Steganography Based on Semantic-Controlled Text-to-Image Generation
Li Xiao, Liquan Chen, Tong Fu, Zhangjie Fu, Yuan Gao · IEEE Transactions on Circuits and Systems for Video Technology · 2025
Artificial Intelligence Generated Content (AIGC) has created a fertile ground for image steganography. Existing Coverless Image Steganography (CIS) methods rely on image semantics to encode secrets, transmitting stego images without embedding, inherently resisting steganalysis. However, constructing CIS Datasets (CISDs) for these methods demands excessive resources, making them impractical for communication. Moreover, achieving low cost and high security is unattainable under these conditions. Therefore, we propose a CIS method based on semantic-controlled text-to-image generation. Our method disguises users as typical AIGC community members utilizing mainstream black-box text-to-image generation with Stable Diffusion (SD). During pre-processing, plain prompts, derived from dialogues with a large language model, are divided into coded and uncoded prompts through our encryption process, where a secret key determines coded prompts. In communication, confusion prompts are selected from uncoded and coded prompts, excluding those determined by secrets. Subsequently, our stego shuffling process combines topic, secret, and confusion prompts to produce stego prompt sets. Diverse stego images maintaining visual topic consistency are generated from these sets using SD with generation seeds indicating transmission order. By introducing confusion prompts, our method is secure from recognition when revealing stego prompts. Experimental results demonstrate our method achieves low communication costs and enhances communication security.