Efficient Neural Network Verification with Exactness Characterization

Abstract

Remarkable progress has been made on verification of neural networks, i.e., showing that neural networks are provably consistent with specifications encoding properties like adversarial robustness. Recent methods developed for scalable neural network verification are based on computing an upper bound on the worst-case violation of the specification. Semidefinite programming (SDP) has been proposed as a means to obtain tight upper bounds. However, SDP solvers do not scale to large neural networks. We introduce a Lagrangian relaxation based on the SDP formulation and a novel algorithm to solve the relaxation that scales to networks that are two orders of magnitude larger than the off-the-shelf SDP solvers. Although verification of neural networks is known to be NP-hard in general, we develop the first known sufficient conditions under which a polynomial time verification algorithm (based on the above relaxation) is guaranteed to perform exact verification (i.e., either verify a property or establish it is untrue). The algorithm can be implemented using primitives available readily in common deep learning frameworks. Experiments show that the algorithm is fast, and is able to compute tight upper bounds on the error rates under adversarial attacks of convolutional networks trained on MNIST and CIFAR-10.