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FL-TENB4:一种联合学习增强的微小高效NetB4-Lite方法,用于在CCTV环境中检测深度假冒.

Jimin Ha1, Abir El Azzaoui1, Jong Hyuk Park1

  • 1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Sensors (Basel, Switzerland)
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概括

这项研究介绍了FL-TENB4,一种新的系统,用于检测使用微型机器学习 (TinyML) 和联合学习 (FL) 的CCTV录像中的深度假冒. 它为资源有限的摄像机提供实时,保护隐私的深度假冒检测.

关键词:
监控系统环境 监控环境有效的网络B4联邦学习学习 (Federated Learning) 是一种学习方式.在TinyML中使用TinyML.深度假冒检测的检测

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科学领域:

  • 计算机视觉和人工智能的人工智能
  • 网络安全和隐私 网络安全和隐私

背景情况:

  • 监控系统对于公共安全至关重要,但容易受到深度假冒操纵的影响.
  • 现有的深度假冒检测方法是计算密集的,阻碍了实时CCTV应用.
  • 深度假冒威胁到视频证据的完整性和个人隐私.

研究的目的:

  • 为CCTV开发一个高效且保护隐私的深度假冒检测框架.
  • 在资源有限的环境中解决当前深度假冒检测解决方案的局限性.

主要方法:

  • 拟议的FL-TENB4框架将微型机器学习 (TinyML) 与EfficientNetB4-Lite集成在一起.
  • 利用联合学习 (FL) 进行保护隐私的协作模式培训.
  • 实现了一种针对边缘设备和实时处理优化的轻量级模型.

主要成果:

  • 在FaceForensics++数据集上,FL-TENB4表现出高深度假冒检测准确度.
  • 实现显著减少模型大小和较低的推理延迟.
  • 验证适用于现实世界,资源有限的CCTV环境.

结论:

  • 在CCTV系统中,FL-TENB4为实时深度假冒检测提供了有效的解决方案.
  • 该框架平衡了性能,效率和数据隐私.
  • 能够提高监控系统对抗深度假冒威胁的安全性和可靠性.