Verifying Binarized Neural Networks by Angluin-Style Learning.

Andy Shih, Adnan Darwiche, Arthur Choi

Abstract: We consider the problem of verifying the behavior of binarized neural networks on some input region. We propose an Angluin-style learning algorithm to compile a neural network on a given region into an Ordered Binary Decision Diagram (OBDD), using a SAT solver as an equivalence oracle. The OBDD allows us to efficiently answer a range of verification queries, including counting, computing the probability of counterexamples, and identifying common characteristics of counterexamples. We also present experimental results on verifying binarized neural networks that recognize images of handwritten digits.