Detection of adversarial attacks on machine-learning systems
Matthew Judah, Jennifer M. Sierchio, M. Planer · 2023
One of the major issues limiting the adoption of machine learning (ML) in applications where accuracy is critical is failure of an otherwise accurate system. Recent work has developed tools to independently measure the competency of machine learning models and the conditions that drive these competency differences. The purpose of this paper is to explore ways to detect and mitigate adversarial attacks by using the same tools to assess competency. We will introduce BAE System’s MindfuL software which assesses ML competency under varying environmental conditions. We then consider a few different types of adversarial attacks and describe detection experiments. We examine the predicted performance and strategy and use that information to detect adversarial attacks. We will present the results of these experiments and discuss the implications of this work and potential future directions for this research.