LLMs enhance medical image compression with integrated steganography

Researchers have developed a novel framework that combines lossless compression and steganography for medical images, leveraging large language models (LLMs). This approach aims to improve compression efficiency and security, which are critical for medical data. The method partitions images into segments to provide global and local modalities for dual-path compression, incorporating a steganography algorithm within the local path to embed privacy messages securely. Extensive experiments show the method's superiority in compression ratios, efficiency, and security, with the source code to be made publicly available. AI

IMPACT This research could lead to more secure and efficient methods for handling sensitive medical image data.

RANK_REASON Academic paper detailing a novel method for image compression and steganography using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Bit Plane Slicing
• large-language models
• Meng Yang

• paper
• other

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