TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security
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}.
Keywords
Cite
@article{arxiv.2510.11301,
title = {TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security},
author = {Junhua Zhou and Quanjun Li and Weixuan Li and Guang Yu and Yihua Shao and Yihang Dong and Mengqian Wang and Zimeng Li and Changwei Gong and Xuhang Chen},
journal= {arXiv preprint arXiv:2510.11301},
year = {2025}
}
Comments
Accepted By BIBM 2025