Polynomial Time Cryptanalytic Extraction of Neural Network Models
Adi Shamir Isaac Canales-Martinez Anna Hambitzer Jorge Chavez-Saab Francisco Rodrigez-Henriquez Nitin Satpute
Billions of dollars and countless GPU hours are currently spent on training
Deep Neural Networks (DNNs) for a variety of tasks. Thus, it is essential to
determine the difficulty of extracting all the parameters of such neural
networks when given access to their black-box implementations. Many versions of
this problem have been studied over the last 30 years, and the best current
attack on ReLU-based deep neural networks was presented at Crypto 2020 by
Carlini, Jagielski, and Mironov. It resembles a differential chosen plaintext
attack on a cryptosystem, which has a secret key embedded in its black-box
implementation and requires a polynomial number of queries but an exponential
amount of time (as a function of the number of neurons). In this paper, we
improve this attack by developing several new techniques that enable us to
extract with arbitrarily high precision all the real-valued parameters of a
ReLU-based DNN using a polynomial number of queries and a polynomial amount of
time. We demonstrate its practical efficiency by applying it to a full-sized
neural network for classifying the CIFAR10 dataset, which has 3072 inputs, 8
hidden layers with 256 neurons each, and over million neuronal parameters. An
attack following the approach by Carlini et al. requires an exhaustive search
over 2 to the power 256 possibilities. Our attack replaces this with our new
techniques, which require only 30 minutes on a 256-core computer.