TeD-SPAD: Temporal Distinctiveness for Self-supervised
  Privacy-preservation for video Anomaly Detection
Joseph Fioresi Ishan Rajendrakumar Dave Mubarak Shah
Video anomaly detection (VAD) without human monitoring is a complex computer
vision task that can have a positive impact on society if implemented
successfully. While recent advances have made significant progress in solving
this task, most existing approaches overlook a critical real-world concern:
privacy. With the increasing popularity of artificial intelligence
technologies, it becomes crucial to implement proper AI ethics into their
development. Privacy leakage in VAD allows models to pick up and amplify
unnecessary biases related to people's personal information, which may lead to
undesirable decision making. In this paper, we propose TeD-SPAD, a
privacy-aware video anomaly detection framework that destroys visual private
information in a self-supervised manner. In particular, we propose the use of a
temporally-distinct triplet loss to promote temporally discriminative features,
which complements current weakly-supervised VAD methods. Using TeD-SPAD, we
achieve a positive trade-off between privacy protection and utility anomaly
detection performance on three popular weakly supervised VAD datasets:
UCF-Crime, XD-Violence, and ShanghaiTech. Our proposed anonymization model
reduces private attribute prediction by 32.25% while only reducing frame-level
ROC AUC on the UCF-Crime anomaly detection dataset by 3.69%. Project Page:
https://joefioresi718.github.io/TeD-SPAD_webpage/