Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability. (arXiv:2208.11264v1 [cs.LG])

Time series anomaly detection is extensively studied in statistics,
economics, and computer science. Over the years, numerous methods have been
proposed for time series anomaly detection using deep learning-based methods.
Many of these methods demonstrate state-of-the-art performance on benchmark
datasets, giving the false impression that these systems are robust and
deployable in many practical and industrial real-world scenarios. In this
paper, we demonstrate that the performance of state-of-the-art anomaly
detection methods is degraded substantially by adding only small adversarial
perturbations …

adversarial anomaly detection awareness detection lg vulnerability