Obfuscated Action Detection: A Privacy-Preserving Approach to Human Activity Anomaly Analysis
Gazi mohammad ismail · 2025
Privacy-preserving human activity anomaly detection has become increasingly important in sensitive applications such as video surveillance, healthcare monitoring, and assisted living systems. While human action detection techniques offer substantial benefits for automated video and sensor-based analysis, they also raise privacy concerns when deployed in environments that require confidentiality. This paper introduces Obfuscated Action Detection, an innovative framework incorporating a temporal obfuscation component based on Generative Adversarial Networks (GANs) to anonymize sensor data. By leveraging Deep Neural Networks, this framework ensures both high accuracy in anomaly detection and feasibility for real-time application. 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.Keywords: HAR; Human action recognition; privacy preserving; GAN; generative adversarial network; image segmentation;