systems, the robustness of neural networks under external disturbance receives more and
more concerns. Global robustness is a robustness property defined on the entire input
domain. And a certified globally robust network can ensure its robustness on any possible
network input. However, the state-of-the-art global robustness certification algorithm can
only certify networks with at most several thousand neurons. In this paper, we propose the …