TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security

2025-10-13

AI Summary

Medical images face significant data security risks in telemedicine and cloud storage, where conventional chaos-based encryption methods suffer from insufficient randomness and weak resistance to cryptanalysis. To address these limitations, this paper proposes a deep learning–enhanced chaotic image encryption architecture. It integrates an LSTM network with a 1D Sine-Quadratic chaotic map to generate high-randomness pseudorandom sequences; introduces an adaptive three-dimensional diffusion algorithm (TDA) to achieve strong inter-channel and inter-pixel confusion; and supports end-to-end scrambling-diffusion encryption for medical images of arbitrary dimensions. Experimental results demonstrate substantial improvements in ciphertext statistical uniformity and differential sensitivity—outperforming state-of-the-art schemes in NPCR, UACI, and information entropy metrics. The method achieves an optimal balance among security, computational efficiency, and practical applicability, and its implementation is publicly available.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Applications of cryptography
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The rise of digital medical imaging, like MRI and CT, demands strong encryption to protect patient data in telemedicine and cloud storage. Chaotic systems are popular for image encryption due to their sensitivity and unique characteristics, but existing methods often lack sufficient security. This paper presents the Three-dimensional Diffusion Algorithm and Deep Learning Image Encryption system (TDADL-IE), built on three key elements. First, we propose an enhanced chaotic generator using an LSTM network with a 1D-Sine Quadratic Chaotic Map (1D-SQCM) for better pseudorandom sequence generation. Next, a new three-dimensional diffusion algorithm (TDA) is applied to encrypt permuted images. TDADL-IE is versatile for images of any size. Experiments confirm its effectiveness against various security threats. The code is available at href{https://github.com/QuincyQAQ/TDADL-IE}{https://github.com/QuincyQAQ/TDADL-IE}.

Problem

• Enhancing medical image encryption security using chaotic systems
• Developing 3D diffusion algorithm for encrypted image protection
• Creating versatile encryption method for medical images of any size

Innovation

• LSTM network with 1D-SQCM for chaotic sequence generation
• Three-dimensional diffusion algorithm for image encryption
• Versatile encryption system for medical images of any size