Work

Adversarial Attacks for Drift Detection

Year: 2025

Type: conference-paper

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 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.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-82

Authors: Fabian Hinder, Valerie Vaquet, Barbara Hammer

Institution: Bielefeld University

Language: English

Topic: Data Stream Mining Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Bundesministerium für Bildung und Forschung, H2020 European Research Council

Award: Smart Water Futures: designing the next generation of urban drinking water systems