  Many approaches for verifying input-output properties of neural networks have
been proposed recently. However, existing algorithms do not scale well to large
networks. Recent work in the field of model compression studied binarized
neural networks (BNNs), whose parameters and activations are binary. BNNs tend
to exhibit a slight decrease in performance compared to their full-precision
counterparts, but they can be easier to verify. This paper proposes a simple
mixed integer programming formulation for BNN verification that leverages
network structure. We demonstrate our approach by verifying properties of BNNs
trained on the MNIST dataset and an aircraft collision avoidance controller. We
compare the runtime of our approach against state-of-the-art verification
algorithms for full-precision neural networks. The results suggest that the
difficulty of training BNNs might be worth the reduction in runtime achieved by
our verification algorithm.
