  Anomaly detection in videos is an important computer vision problem with
various applications including automated video surveillance. Although
adversarial attacks on image understanding models have been heavily
investigated, there is not much work on adversarial machine learning targeting
video understanding models and no previous work which focuses on video anomaly
detection. To this end, we investigate an adversarial machine learning attack
against video anomaly detection systems, that can be implemented via an
easy-to-perform cyber-attack. Since surveillance cameras are usually connected
to the server running the anomaly detection model through a wireless network,
they are prone to cyber-attacks targeting the wireless connection. We
demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform
and effective denial-of-service (DoS) attack, can be utilized to generate
adversarial data for video anomaly detection systems. Specifically, we apply
several effects caused by the Wi-Fi deauthentication attack on video quality
(e.g., slow down, freeze, fast forward, low resolution) to the popular
benchmark datasets for video anomaly detection. Our experiments with several
state-of-the-art anomaly detection models show that the attackers can
significantly undermine the reliability of video anomaly detection systems by
causing frequent false alarms and hiding physical anomalies from the
surveillance system.
