Eager Falsification for Accelerating Robustness Verification of Deep Neural Networks

Formal robustness verification of deep neural networks (DNNs) is a promising approach for achieving a provable reliability guarantee to AI-enabled software systems. Limited scalability is one of the main obstacles to the verification problem. In this paper, we propose eager falsification to accelerate the robustness verification of DNNs. It divides the verification problem into a set of independent subproblems and solves them in descending order of their falsification probabilities. Once a subproblem is falsified, the verification terminates with a conclusion that the network is not robust. We introduce a notion of label affinity to measure the falsification probability and present an approach to computing the probability based on symbolic interval propagation. Our approach is orthogonal to existing verification techniques. We integrate it into four state-of-the-art verification tools, i.e., MIPVerify, Neurify, DeepZ, and DeepPoly, and conduct extensive experiments on 8 benchmark datasets. The experimental results show that our approach can significantly improve these tools by up to 200x speedup when the perturbation distance is in a reasonable range.