<title>Abstract</title> Video Anomaly Detection (VAD) aims to identify unexpected behaviors or objects in videos. Due to the lack of available anomaly samples for training, video anomaly detection is often considered as a one-class classification problem. Specifically, an autoencoder is trained only on normal data, expected to produce large reconstruction errors when detecting anomalies. However, autoencoders can often learn to reconstruct anomalies, leading to detection failures. To address this issue, we introdu…