用正交投影去除视频异常检测中的隐私信息，兼顾准确率与隐私保护。
Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection
- 引入正交投影层，专注异常相关特征，忽略无关信息。
- 弱监督下抑制人脸属性，保留姿态和运动等非识别特征。
- 提出隐私评估框架，可量化分析敏感信息过滤效果。
视频异常检测（VAD）系统常以准确性为先，忽视隐私问题，限制了实际应用。本文提出轻量级正交投影层（OPL），通过消除任务无关变化，生成聚焦异常线索的表示。针对以人为中心场景的隐私风险，引入引导式OPL（G-OPL），利用面部存在信号的弱监督，抑制人脸属性，同时保留姿态、运动等非识别特征。采用余弦对齐目标，在无身份标签或对抗训练条件下，确保面部信息的一致性捕捉与移除。进一步提出隐私感知评估框架，联合评估检测性能与隐私保护能力，并支持敏感信息过滤过程的分析。实验表明，将隐私约束融入模型设计可在保持甚至提升检测准确率的同时显著减少敏感信息，验证了基于投影架构在隐私感知VAD中的有效性。
原文摘要 · Abstract (English)
Video anomaly detection (VAD) systems often prioritize accuracy while overlooking privacy concerns, limiting their suitability for real-world deployment. We propose the Orthogonal Projection Layer (OPL), a lightweight module that removes task-irrelevant variations to produce representations focused on anomaly-relevant cues. To address privacy risks in human-centered scenarios, we introduce Guided OPL (G-OPL), which suppresses facial attributes using weak supervision from face-presence signals while preserving non-identifying features such as pose and motion. A cosine alignment objective enforces consistent capture and removal of facial information without identity labels or adversarial training. We further present a privacy-aware evaluation framework that jointly assesses detection performance and privacy preservation, and enables analysis of how sensitive information is filtered. Experiments show that embedding privacy constraints into model design reduces sensitive information while maintaining or improving detection accuracy, supporting projection-based architectures as a principled approach for privacy-aware VAD.
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