246a3c5544feb054f3ea718f61adfa16-Paper.pdf

Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories:exact verifiers that run in exponential time andrelaxed verifiers that are efficient but incomplete. In this paper, we unify all existing LP-relaxed verifiers, to the best of our knowledge, under a general convex relaxation framework.