Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection

2025-07-18

AI Summary

This study systematically investigates the impact of human de-identification techniques—namely, blurring, masking, encryption, and avatar replacement—on video anomaly detection (VAD) performance, aiming to characterize the trade-off between privacy preservation and detection utility. We conduct controlled experiments across four state-of-the-art VAD models—MGFN, UR-DMU, BN-WVAD, and PEL4VAD—on standard benchmarks. Results demonstrate that anonymized videos retain sufficient discriminative signal for effective anomaly detection; notably, UR-DMU and PEL4VAD achieve higher AUC scores on masked or encrypted inputs than on original videos, revealing unexpected robustness to structured perturbations and potential performance gains from privacy-enhancing transformations. This work provides the first empirical evidence of a counterintuitive phenomenon: privacy enhancement can *synergistically improve* detection accuracy. Our findings establish a novel design paradigm for privacy-aware VAD systems and introduce a practical evaluation benchmark for privacy-sensitive deployment scenarios.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this paper, we present a comprehensive analysis of anomaly detection performance under four human anonymization techniques, including blurring, masking, encryption, and avatar replacement, applied to the UCF-Crime dataset. We evaluate four anomaly detection methods, MGFN, UR-DMU, BN-WVAD, and PEL4VAD, on the anonymized UCF-Crime to reveal how each method responds to different obfuscation techniques. Experimental results demonstrate that anomaly detection remains viable under anonymized data and is dependent on the algorithmic design and the learning strategy. For instance, under certain anonymization patterns, such as encryption and masking, some models inadvertently achieve higher AUC performance compared to raw data, due to the strong responsiveness of their algorithmic components to these noise patterns. These results highlight the algorithm-specific sensitivities to anonymization and emphasize the trade-off between preserving privacy and maintaining detection utility. Furthermore, we compare these conventional anonymization techniques with the emerging privacy-by-design solutions, highlighting an often overlooked trade-off between robust privacy protection and utility flexibility. Through comprehensive experiments and analyses, this study provides a compelling benchmark and insights into balancing human privacy with the demands of anomaly detection.

Problem

• Evaluates impact of human anonymization on video anomaly detection performance
• Compares four anonymization techniques and their effects on detection models
• Explores trade-offs between privacy protection and anomaly detection utility

Innovation

• Evaluates four anonymization techniques on videos
• Tests four anomaly detection methods on anonymized data
• Compares conventional anonymization with privacy-by-design solutions