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The integration of machine learning (ML) in numerous critical applicationsintroduces a range of privacy concerns for individuals who provide theirdatasets for model training. One such privacy risk is Membership Inference(MI), in which an attacker seeks to determine whether a particular data samplewas included in the training dataset of a model. Current state-of-the-art MIattacks capitalize on access to the model's predicted confidence scores tosuccessfully perform membership inference, and employ data poisoning to furtherenhance their effectiveness. In this work, we focus on the less explored andmore realistic label-only setting, where the model provides only the predictedlabel on a queried sample. We show that existing label-only MI attacks areineffective at inferring membership in the low False Positive Rate (FPR)regime. To address this challenge, we propose a new attack Chameleon thatleverages a novel adaptive data poisoning strategy and an efficient queryselection method to achieve significantly more accurate membership inferencethan existing label-only attacks, especially at low FPRs.

Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning