Tomato

Tomato is a proof-of-concept for a steganography tool that utilizes minimum entropy coupling codes provided by ssokota. This tool achieves information hiding by merging the probability distribution of the hidden information (ciphertext) with the probability distribution of the cover text generated by a large language model (LLM). This coupling minimizes the joint entropy and ensures that the steganographic text (cover text and embedded information) retains the statistical properties of natural language, making the hidden information difficult to detect. During the decoding process, LLM assists by providing context-aware interpretations, and then uses MEC to reversely decouple the hidden information in the cover text. This approach ensures that hidden information can be seamlessly integrated into the text and can be retrieved later safely and accurately with minimal risk.

Tomato is suitable for users who need to securely hide and retrieve information from text, such as cybersecurity experts, data protection officers, or any individual who needs to communicate covertly. The tool's stealth and natural language-based nature make it ideal for these users.