  Machine Learning-as-a-Service (MLaaS) has become a widespread paradigm,
making even the most complex machine learning models available for clients via
e.g. a pay-per-query principle. This allows users to avoid time-consuming
processes of data collection, hyperparameter tuning, and model training.
However, by giving their customers access to the (predictions of their) models,
MLaaS providers endanger their intellectual property, such as sensitive
training data, optimised hyperparameters, or learned model parameters.
Adversaries can create a copy of the model with (almost) identical behavior
using the the prediction labels only. While many variants of this attack have
been described, only scattered defence strategies have been proposed,
addressing isolated threats. This raises the necessity for a thorough
systematisation of the field of model stealing, to arrive at a comprehensive
understanding why these attacks are successful, and how they could be
holistically defended against. We address this by categorising and comparing
model stealing attacks, assessing their performance, and exploring
corresponding defence techniques in different settings. We propose a taxonomy
for attack and defence approaches, and provide guidelines on how to select the
right attack or defence strategy based on the goal and available resources.
Finally, we analyse which defences are rendered less effective by current
attack strategies.
