Paper 2025/1231

Compressing steganographic payloads with LLM assistance

Abstract

Steganography is the practice of concealing messages or information within other non-secret text or media to avoid detection. A central challenge in steganography is balancing payload size with detectability and media constraints—larger payloads increase the risk of detection and require proportionally larger or higher-capacity carriers. In this paper, we introduce a novel approach that combines Huffman coding, suitable dictionary identification, and large language models (LLMs) rephrasing techniques to significantly reduce payload size. This enables more efficient use of limited-capacity carriers, such as images, while minimizing the visual or statistical footprint. Our method allows for the embedding of larger payloads into fixed-size media, addressing a key bottleneck in traditional steganographic systems. By optimizing payload compression prior to encoding, we improve both the stealth and scalability of steganographic communication.