TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911207370260480 |
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| author | Zhou, Junhua Li, Quanjun Li, Weixuan Yu, Guang Shao, Yihua Dong, Yihang Wang, Mengqian Li, Zimeng Gong, Changwei Chen, Xuhang |
| author_facet | Zhou, Junhua Li, Quanjun Li, Weixuan Yu, Guang Shao, Yihua Dong, Yihang Wang, Mengqian Li, Zimeng Gong, Changwei Chen, Xuhang |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security Zhou, Junhua Li, Quanjun Li, Weixuan Yu, Guang Shao, Yihua Dong, Yihang Wang, Mengqian Li, Zimeng Gong, Changwei Chen, Xuhang Cryptography and Security 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}. |
| title | TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.11301 |