  Machine Learning (ML) models are increasingly deployed in the wild to perform
a wide range of tasks. In this work, we ask to what extent can an adversary
steal functionality of such "victim" models based solely on blackbox
interactions: image in, predictions out. In contrast to prior work, we present
an adversary lacking knowledge of train/test data used by the model, its
internals, and semantics over model outputs. We formulate model functionality
stealing as a two-step approach: (i) querying a set of input images to the
blackbox model to obtain predictions; and (ii) training a "knockoff" with
queried image-prediction pairs. We make multiple remarkable observations: (a)
querying random images from a different distribution than that of the blackbox
training data results in a well-performing knockoff; (b) this is possible even
when the knockoff is represented using a different architecture; and (c) our
reinforcement learning approach additionally improves query sample efficiency
in certain settings and provides performance gains. We validate model
functionality stealing on a range of datasets and tasks, as well as on a
popular image analysis API where we create a reasonable knockoff for as little
as $30.
