Plausible Deniable Medical Image Encryption by Large Language Models and Reversible Content-Aware Strategy

There is a rising concern about healthcare system security, where data loss could bring lots of damages to patients and hospitals. As a promising encryption method for medical images, DNA encoding own characteristics of high speed, parallelism computation, minimal storage, and unbreakable cryptosystems. Inspired by the idea of involving Large Language Models(LLMs) to improve DNA encoding, we propose a medical image encryption method with LLM-enhanced DNA encoding, which consists of LLM enhancing module and content-aware permutation&diffusion module. Regarding medical images generally have plain backgrounds with low-entropy pixels, the first module compresses pixels into highly compact signals with features of probabilistic varying and plausibly deniability, serving as another LLM-based layer of defense against privacy breaches before DNA encoding. The second module not only adds permutation by randomly sampling from a redundant correlation between adjacent pixels to break the internal links between pixels but also performs a DNA-based diffusion process to greatly increase the complexity of cracking. Experiments on ChestXray-14, COVID-CT and fcon-1000 datasets show that the proposed method outperforms all comparative methods in sensitivity, correlation and entropy.