Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation

The robustness of neural networks can be quantitatively indicated by a lower bound within which any perturbation does not alter the original input's classification result. A certified lower bound is also a criterion to evaluate the performance of robustness verification approaches. In this paper, we present a tighter linear approximation approach for the robustness verification of Convolutional Neural Networks (CNNs). By the tighter approximation, we can tighten the robustness verification of CNNs, i.e., proving they are robust within a larger perturbation distance. Furthermore, our approach is applicable to general sigmoid-like activation functions. We implement DEEPCERT, the resulting verification toolkit. We evaluate it with open-source benchmarks, including LeNet and the models trained on MNIST and CIFAR. Experimental results show that DEEPCERT outperforms other state-of-the-art robustness verification tools with at most 286.3% improvement to the certified lower bound and 1566.8 times speedup for the same neural networks.