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适应性图像加密方法使用增强的群集智能算法.

Sachin Minocha1, Suvita Rani Sharma1, Birmohan Singh2

  • 1School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.

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概括

本研究介绍了一种基于Iterative Cosine操作符的河马优化 (ICO-HO) 算法,用于优化图像加密中的混乱地图参数. 这种新的方法提高了医疗图像的安全性和随机性,优于现有的方法.

关键词:
一个混乱的混沌.河马的优化优化超光谱图像 超光谱图像 超光谱图像图像加密 图像加密代代号运算符 代号运算符医学图像 医学图像在PWCM中使用PWCM.在PWLCMM中,使用PWLCM.

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科学领域:

  • 密码学 密码学 密码学 密码学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 基于混乱的加密利用独特的混乱特性来安全传输数据.
  • 最佳选择初始和控制参数对于基于混乱的加密方法的性能至关重要.
  • 对于复杂的参数空间,现有的优化技术可能缺乏足够的探索和利用能力.

研究的目的:

  • 为优化混乱的地图参数提出一个基于Iterative Cosine运算符的河马优化 (ICO-HO) 算法.
  • 设计一个自适应的图像加密方法,利用优化的混乱地图.
  • 提高图像加密的安全性和性能,特别是在医学成像应用中.

主要方法:

  • 开发ICO-HO算法,整合一个新的第4阶段的位置更新,并采用基于对立的学习.
  • 使用ICO-HO优化PWLCM和PWCM混乱图的参数,用于图像加密中的密钥生成.
  • 使用视觉,统计,差异和定量分析对各种医疗图像 (灰度,RGB,超光谱) 进行拟议的加密方法的评估.

主要成果:

  • 与标准的河马优化 (HO) 相比,ICO-HO算法在CEC-2017基准函数上表现优越.
  • 拟议的加密方法实现了高的NPCR (99.60%) 和UACI (33.40%) 值,表明对差异性攻击有很强的抵抗力.
  • 该方法实现了优异的值 (8.0位图像为7.9995,16位图像为15.8124),超过了最先进的技术.

结论:

  • ICO-HO算法有效地优化混乱的地图参数,以实现强大的图像加密.
  • 拟议的自适应图像加密方法提供了增强的安全性和高随机性,适用于敏感的医疗数据.
  • ICO-HO代表了对加密应用的元启发式优化的重大进步.