Trustworthy Video Anomaly Detection: A Privacy-Aware, Explainable, and Efficient Deep Learning Framework

Video anomaly detection (VAD) increasingly relies on high-capacity spatio-temporal models, yet accuracy-only evaluation is inadequate when automated alerts can affect safety, privacy, or human decision making. This paper introduces TAP-VAD, a trustworthy-AI design framework that treats detection quality, calibrated uncertainty, robustness, explainability, privacy, computational efficiency, and human oversight as coupled requirements rather than post-hoc additions. The architecture combines an adaptable video encoder, bounded-cost temporal memory, anomaly and open-vocabulary semantic heads, uncertainty calibration, layered spatial–temporal–semantic explanations, and a cross-silo federated learning option with secure aggregation. Its principal methodological contribution is an auditable evaluation protocol that specifies how each trust dimension should be measured, stress-tested, and reported on representative video anomaly benchmarks. The literature synthesis identifies three recurring gaps: trust properties are commonly evaluated in isolation, privacy mechanisms are rarely co-designed with explanation, and long-video efficiency is seldom linked to uncertainty-aware escalation. TAP-VAD therefore adds a policy layer that can abstain and route ambiguous cases to human review instead of converting every score into an automatic decision. The framework does not claim unperformed benchmark gains; it provides a falsifiable architecture, ablation plan, threat model, and reproducibility checklist for empirical validation. The resulting perspective connects computer-vision performance with epistemic and governance requirements for trustworthy anomaly detection