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Machine learning (ML) models may be deemed confidential due to theirsensitive training data, commercial value, or use in security applications.Increasingly often, confidential ML models are being deployed with publiclyaccessible query interfaces. ML-as-a-service ("predictive analytics") systemsare an example: Some allow users to train models on potentially sensitive dataand charge others for access on a pay-per-query basis.  The tension between model confidentiality and public access motivates ourinvestigation of model extraction attacks. In such attacks, an adversary withblack-box access, but no prior knowledge of an ML model's parameters ortraining data, aims to duplicate the functionality of (i.e., "steal") themodel. Unlike in classical learning theory settings, ML-as-a-service offeringsmay accept partial feature vectors as inputs and include confidence values withpredictions. Given these practices, we show simple, efficient attacks thatextract target ML models with near-perfect fidelity for popular model classesincluding logistic regression, neural networks, and decision trees. Wedemonstrate these attacks against the online services of BigML and AmazonMachine Learning. We further show that the natural countermeasure of omittingconfidence values from model outputs still admits potentially harmful modelextraction attacks. Our results highlight the need for careful ML modeldeployment and new model extraction countermeasures.

Stealing Machine Learning Models via Prediction APIs