  The robustness of convolutional neural networks (CNNs) is vital to modern
AI-driven systems. It can be quantified by formal verification by providing a
certified lower bound, within which any perturbation does not alter the
original input's classification result. It is challenging due to nonlinear
components, such as MaxPool. At present, many verification methods are sound
but risk losing some precision to enhance efficiency and scalability, and thus,
a certified lower bound is a crucial criterion for evaluating the performance
of verification tools. In this paper, we present MaxLin, a robustness verifier
for MaxPool-based CNNs with tight linear approximation. By tightening the
linear approximation of the MaxPool function, we can certify larger certified
lower bounds of CNNs. We evaluate MaxLin with open-sourced benchmarks,
including LeNet and networks trained on the MNIST, CIFAR-10, and Tiny ImageNet
datasets. The results show that MaxLin outperforms state-of-the-art tools with
up to 110.60% improvement regarding the certified lower bound and 5.13 $\times$
speedup for the same neural networks. Our code is available at
https://github.com/xiaoyuanpigo/maxlin.
