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.

Authors

• Yiting Wu
• Min Zhang College of Intelligence and Computing, Tianjin University

Keywords

No keywords are indexed for this paper.