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基于3D-MNFC混乱系统和物流地图的双区域加密模型

Jingyan Li1, Yan Niu1, Dan Yu1

  • 1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China.

Entropy (Basel, Switzerland)
|February 27, 2026
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概括
此摘要是机器生成的。

本研究引入了对肖像图像的新型双区域加密模型,提高了安全性和效率. 这种新方法通过单独处理面部和非面部区域来显著提高加密速度.

关键词:
在3D-MNFC中使用3D-MNFC.美国有线电视新闻网 (CNN) 关键字生成这是DNA编码的DNA.MTCNN 面部检测 面部检测双区域加密的双区域加密.动态分析 动态分析安全分析安全分析.

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科学领域:

  • 计算机科学 计算机科学
  • 信息安全 信息安全
  • 图像处理 图像处理

背景情况:

  • 面部信息隐私至关重要,需要安全的加密方法.
  • 传统的图像加密是低效的,处理整个图像,无论敏感的内容.
  • 现有的方法通常会对肖像图像安全性施加不必要的计算负担.

研究的目的:

  • 为肖像图像开发一个高效和安全的双区域加密模型.
  • 为了解决传统的全图像加密方法的低效.
  • 为了保护敏感的面部信息,同时优化计算资源.

主要方法:

  • 利用多任务级联卷积网络 (MTCNN) 进行面部和非面部区域细分.
  • 开发了面部区域的强大加密方案,使用基于CNN的密钥生成器,3D-MNFC混乱系统,DNA编码和位逆转.
  • 使用物流地图与XOR操作来有效加密非面部区域.

主要成果:

  • 获得了很大的键空间 (2^536) 和接近理想的信息 (7.9995).
  • 通过NPCR (99.6055%) 和UACI (33.4599%) 值证明了高安全性.
  • 与传统方法相比,加密效率至少提高了37.82%.

结论:

  • 拟议的双区域加密模型为肖像图像的效率和安全性提供了显著的改进.
  • 这种方法有效地平衡了对敏感面部数据的强有力的保护需求与计算效率的需求.
  • 该模型为隐私敏感应用程序中安全和快速的图像加密提供了实用解决方案.