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Updated: Jan 8, 2026

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超级混乱和摩尔自动机与黄金序列的融合,用于增强医学图像加密
Mohamed Gabr1, Eyad Mamdouh2, Dina El-Damak3
1Computer Science Department, Faculty of Media Engineering and Technology, German University in Cairo (GUC), New Cairo, 11835, Egypt.
Scientific reports
|December 12, 2025
概括
这项研究引入了一种新的加密方法,用于安全地将医疗图像传输到云服务. 这种先进的技术使用的是Memristive Coupled Neural Network Model (MCNNM) 和摩尔神经网络模型.
科学领域:
- 计算机科学 计算机科学
- 密码学 密码学 密码学
- 医疗信息学 医疗信息学
背景情况:
- 敏感医疗图像的安全传输对于基于云的医疗保健至关重要.
- 现有的加密方法可能无法充分保护对复杂的威胁.
研究的目的:
- 开发和评估一种新的,强大的加密方法,用于安全的医疗图像云传输.
- 加强医疗保健系统中的数据安全和隐私.
主要方法:
- 图像整合成一个增强图像.
- 使用MCNNM生成的S盒和密钥进行分层加密.
- 摩尔的自动机和黄金序列用于图像编码 (混).
- 在N个周期内进行代加密,以提高安全性.
主要成果:
- 实现了一个大键空间 ([公式:参见文本]).
- 显示了高加密率15.5 Mbps. 显示了高加密率15.5 Mbps.
- 统计测试证实了算法的弹性和安全性.
结论:
- 拟议的加密系统为医疗图像提供了强大的安全性.
- 该方法是务实设计的,用于在云环境中保护敏感的医疗保健数据.
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