Adversarial Attacks for Drift Detection

Abstract

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 how to construct data streams that are drifting without being detected. We refer to those as drift adversarials. In particular, we compute all possible adversairals for common detection schemes and underpin our theoretical findings with empirical evaluations.