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As industrial applications are increasingly automated by machine learningmodels, enforcing personal data ownership and intellectual property rightsrequires tracing training data back to their rightful owners. Membershipinference algorithms approach this problem by using statistical techniques todiscern whether a target sample was included in a model's training set.However, existing methods only utilize the unaltered target sample or simpleaugmentations of the target to compute statistics. Such a sparse sampling ofthe model's behavior carries little information, leading to poor inferencecapabilities. In this work, we use adversarial tools to directly optimize forqueries that are discriminative and diverse. Our improvements achievesignificantly more accurate membership inference than existing methods,especially in offline scenarios and in the low false-positive regime which iscritical in legal settings. Code is available athttps://github.com/YuxinWenRick/canary-in-a-coalmine.

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries