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A Multi-Layered Privacy-Preserving Approach to Obfuscated Human Action Recognition for Robust Anomaly Detection
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  • Gazi mohammad ismail,
  • Zhang Xueping,
  • Yang Junxiang,
  • Li Bin,
  • Yanchuang Zhao
Gazi mohammad ismail
Henan University of Technology School of Information Science and Engineering

Corresponding Author:[email protected]

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Zhang Xueping
Henan University of Technology School of Information Science and Engineering
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Yang Junxiang
Henan University of Technology School of Information Science and Engineering
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Li Bin
Henan University of Technology School of Information Science and Engineering
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Yanchuang Zhao
Henan University of Technology School of Information Science and Engineering
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Abstract

Privacy-preserving human activity anomaly detection has become critical in privacy-sensitive fields like video surveillance, healthcare, and assisted living. While human action recognition offers significant advantages in automated analysis, it raises confidentiality concerns. This paper introduces Obfuscated Action Detection, a novel framework that uses Generative Adversarial Networks (GANs) for temporal obfuscation, ensuring privacy while maintaining high accuracy. By integrating Deep Neural Networks, the framework delivers robust anomaly detection with real-time feasibility. Tested on the UCF101 dataset, the model achieves high accuracy (98.59% to 100%) and strong generalization to unseen data (88.44% test accuracy). With impressive precision (98.14%), recall (99.56%), and F1 score (98.84%), Obfuscated Action Detection effectively balances privacy with performance. The framework shows promise for real-world applications in privacy-critical domains, offering robust privacy protection without compromising detection accuracy. Extensive experiments demonstrate the capability of Obfuscated Action Detection to achieve robust privacy protection without compromising detection precision, making it a viable solution for applications that prioritize both privacy and reliability. Additionally, this paper presents an overview of related works, summarizing recent advancements and methodologies in privacy-preserving anomaly detection.