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

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research, Medical Devices & Health Technology · Depth: Expert, extended

Summary

A novel joint lossless compression and steganography framework for medical images addresses the trade-off between compression performance, efficiency, and security using Large Language Models (LLMs). The system employs an adaptive modalities decomposition strategy, inspired by bit plane slicing, to partition images into global and local segments. A dual-path compression scheme then processes global modalities with variational autoencoders (VAEs) and local modalities with an LLM (Qwen2.5-7B), integrating a segmented message steganography algorithm for secure privacy embedding. An anatomical priors-based low-rank adaptation (A-LoRA) fine-tuning strategy further enhances performance and training speed. Experiments show superior compression ratios (e.g., 4.0% better than DLPR, 7.6%-17.2% better than JPEG-XL) and competitive inference speeds across various medical datasets, achieving high visual fidelity (PSNR 68.79 dB, SSIM 0.999998).

Key takeaway

For AI Security Engineers or Machine Learning Engineers developing medical imaging solutions, this framework offers a critical advancement. You can achieve leading lossless compression ratios while embedding sensitive patient data invisibly and securely within the image itself. This dual capability mitigates privacy risks during transmission and storage, making it a robust option for compliance-focused applications. Consider integrating this dual-path LLM-VAE approach to enhance both data efficiency and security in your systems.

Key insights

Joint lossless compression and steganography for medical images utilizes LLMs and VAEs for superior performance and security.

Principles

• Bit plane slicing enables secure, invisible message embedding.
• Dual-path compression balances LLM power with VAE efficiency.
• Anatomical priors enhance LLM fine-tuning for medical images.

Method

Images are decomposed into global/local modalities via adaptive bit plane slicing. Global modalities are compressed with VAEs, local with LLMs incorporating segmented steganography. A-LoRA fine-tunes the LLM using anatomical priors.

In practice

• Embed patient data securely in medical images.
• Optimize LLM compression for specific domains.
• Improve medical image storage efficiency.

Topics

• Lossless Image Compression
• Medical Imaging
• Steganography
• Large Language Models
• Variational Autoencoders
• A-LoRA Fine-tuning

Code references

• limbee/NTIRE2017

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.