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Machine learning models trained on confidential datasets are increasinglybeing deployed for profit. Machine Learning as a Service (MLaaS) has made suchmodels easily accessible to end-users. Prior work has developed modelextraction attacks, in which an adversary extracts an approximation of MLaaSmodels by making black-box queries to it. However, none of these works is ableto satisfy all the three essential criteria for practical model extraction: (1)the ability to work on deep learning models, (2) the non-requirement of domainknowledge and (3) the ability to work with a limited query budget. We design amodel extraction framework that makes use of active learning and large publicdatasets to satisfy them. We demonstrate that it is possible to use thisframework to steal deep classifiers trained on a variety of datasets from imageand text domains. By querying a model via black-box access for its topprediction, our framework improves performance on an average over a uniformnoise baseline by 4.70x for image tasks and 2.11x for text tasks respectively,while using only 30% (30,000 samples) of the public dataset at its disposal.

A framework for the extraction of Deep Neural Networks by leveraging public data