  We argue that the machine learning problem of model extraction is actually a
cryptanalytic problem in disguise, and should be studied as such. Given oracle
access to a neural network, we introduce a differential attack that can
efficiently steal the parameters of the remote model up to floating point
precision. Our attack relies on the fact that ReLU neural networks are
piecewise linear functions, and thus queries at the critical points reveal
information about the model parameters.
  We evaluate our attack on multiple neural network models and extract models
that are 2^20 times more precise and require 100x fewer queries than prior
work. For example, we extract a 100,000 parameter neural network trained on the
MNIST digit recognition task with 2^21.5 queries in under an hour, such that
the extracted model agrees with the oracle on all inputs up to a worst-case
error of 2^-25, or a model with 4,000 parameters in 2^18.5 queries with
worst-case error of 2^-40.4. Code is available at
https://github.com/google-research/cryptanalytic-model-extraction.

Machine Learning;Cryptography and Security