Adversarial Attacks for Drift Detection

Concept drift refers to the change of data distributions over time. While drift poses a challenge for learning models, requiring their continual adaption, it is also relevant in system monitoring to detect malfunctions, system failures, and unexpected behavior. In the latter case, the robust and reliable detection of drifts is imperative. This work studies the shortcomings of commonly used drift detection schemes. We show that they are prone to adversarial attacks, i.e., streams with undetected drift. In particular, we give necessary and sufficient conditions for their existence, provide methods for their construction, and demonstrate this behavior in experiments.