Joint Lossless Compression and Steganography for Medical Images via Large Language Models

Recently, large language models (LLMs) have driven promis ing progress in lossless image compression. However, di rectly adopting existing paradigms for medical images suf fers from an unsatisfactory trade-off between compressionperformance and efficiency. Moreover, existing LLM-basedcompressors often overlook the security of the compres sion process, which is critical in modern medical scenarios.To this end, we propose a novel joint lossless compressionand steganography framework. Inspired by bit plane slicing(BPS), we find it feasible to securely embed privacy messagesinto medical images in an invisible manner. Based on this in sight, an adaptive modalities decomposition strategy is firstdevised to partition the entire image into two segments, pro viding global and local modalities for subsequent dual-pathlossless compression. During this dual-path stage, we inno vatively propose a segmented message steganography algo rithm within the local modality path to ensure the security ofthe compression process. Coupled with the proposed anatom ical priors-based low-rank adaptation (A-LoRA) fine-tuningstrategy, extensive experimental results demonstrate the su periority of our proposed method in terms of compression ra tios, efficiency, and security. The source code will be madepublicly available.