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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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在智能城市中使用多式联络方法安全地进行面部生物识别身份验证.

Aanjankumar Sureshkumar1, Malathy Sathyamoorthy2, Rajesh Kumar Dhanaraj3

  • 1School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, 466114, Madhya Pradesh, India.

Scientific reports
|December 29, 2025
PubMed
概括

本研究介绍了一种多式联网深度学习模型,用于智能城市的安全面部生物识别身份验证. 该系统将Convolutional神经网络 (CNN) 和ResNet-50与ElGamal加密技术相结合,实现97.1%的精度,防止伪造和增强数据安全.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.埃尔·加马尔 (ElGamal) 是一个面部身份验证是面部认证.这就是ResNet-50的特点.伪造攻击的攻击.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 在智能城市中,面部生物识别安全对于保护公民数据和防止未经授权的访问至关重要.
  • 现有的系统面临着伪造攻击和安全数据传输的挑战.
  • 需要强大的身份验证方法,将特征提取和加密安全性结合起来.

研究的目的:

  • 提出一个多式联网深度学习模型,与加密框架集成,用于增强面部生物识别身份验证.
  • 为了保护面部数据免受伪造攻击,并确保智能城市网络中传输期间的隐私.
  • 评估模型的性能,并与传统方法进行比较.

主要方法:

  • 使用多式深度学习方法,将卷积神经网络 (CNN) 结合为低级特征提取和剩余网络 (ResNet-50) 为高级语义模式识别.
  • 集成ElGamal加密技术以保护提取的面部特征并确保数据隐私.
  • 在CelebA Faces数据集上训练和评估模型.

主要成果:

  • 实现了97.1%的面部映射预测准确度,平均低得分损失为0.04.
  • 与传统模型相比,表现出优越的性能:比CNN准确率高1.2%,比ResNet-50高2.2%,比Brakerski-Gentry-Vaikuntanathan算法高1.1%.
  • 有效地处理了假冒攻击,并确保了安全的数据传输.

结论:

  • 拟议的融合多式联通方法在智能城市环境中显著提高了面部生物识别安全性.
  • 结合CNN,ResNet-50和ElGamal加密技术,提供了一个强大的解决方案,可以防止未经授权的访问,并确保数据隐私.
  • 该模型非常适合未来的智能城市应用,需要先进的安全功能.