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

• URL: http://arxiv.org/abs/2510.11301v1
• Date: Mon, 13 Oct 2025 11:47:55 GMT
• Title: TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security
• Authors: Junhua Zhou, Quanjun Li, Weixuan Li, Guang Yu, Yihua Shao, Yihang Dong, Mengqian Wang, Zimeng Li, Changwei Gong, Xuhang Chen,
• Abstract summary: Digital medical imaging, like MRI and CT, demands strong encryption to protect patient data in telemedicine and cloud storage.<n>Chaotic systems are popular for image encryption due to their sensitivity and unique characteristics, but existing methods often lack sufficient security.<n>This paper presents the Three-dimensional Diffusion Algorithm and Deep Learning Image Encryption system (TDADL-IE), built on three key elements.
• Score: 7.607394734101082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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}.

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