A Semantic Controllable Long Text Steganography Framework Based on LLM Prompt Engineering and Knowledge Graph

With ongoing advancements in natural language technology, text steganography has achieved notable progress. However, existing methods primarily concentrate on the probability distribution between words, often overlooking comprehensive control over text semantics. Particularly in the case of longer texts, these methods struggle to preserve coherence and contextual consistency, thereby increasing the risk of detection in practical applications. To effectively improve steganography security, we propose a semantic controllable long-text steganography framework based on prompt engineering and knowledge graph (KG) integration, obviating supplementary training. This framework leverages triplets from the KG and task descriptions to construct prompts, directing the large language model (LLM) to generate text that aligns with the triplet content. Subsequently, the model effectively embeds secret information by encoding the candidate pools established around the sampled target words. The experimental results demonstrate that our framework ensures the concealment of steganographic text while maintaining the relevance and consistency of the content as expected. Moreover, it can be flexibly adapted to various application scenarios, showcasing its potential and advantages in practical implementations.