QUERY-EFFICIENT DECISION-BASED SPARSE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS

no code implementations • ICLR 2022 • Viet Vo, Ehsan M Abbasnejad, Damith Ranasinghe

The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS).

BIG-bench Machine Learning