Feature Perturbation Agent based Adversarial Attack Method for Weakly Supervised Video Anomaly Detection

Weakly supervised video anomaly detection (WS-VAD) techniques, based on video backbone models, are widely used in surveillance but are vulnerable to adversarial attacks. However, directly applying existing methods causes high memory consumption and low efficiency, and adversarial attacks on WS-VAD models have yet to be specifically studied. In this paper, we pioneer to propose a two-staged Feature Perturbation Agent based Adversarial Attack (FPAgent) method for WS-VAD. To better deceive detection models, we explore the deceivable feature spaces. To describe the locations of the deceivable feature spaces, we propose a feature perturbation agent, which also transforms the complex video-level attack into a simple segment-level attack. Besides, we propose a perturbation guider strategy to guide the feature vectors into the deceivable feature spaces, by computing the perturbation from the first segment of each video. The experiments have verified the effectiveness, as well as the attack efficiency and low memory consumption of our method.