Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation

• URL: http://arxiv.org/abs/2211.09810v1
• Date: Sun, 13 Nov 2022 08:37:13 GMT
• Title: Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation
• Authors: Yuan Xiao, Tongtong Bai, Mingzheng Gu, Chunrong Fang, Zhenyu Chen
• Abstract summary: Ti-Lin is a Tight Linear approximation approach for robustness verification of Conal Neural Networks. We present a new linear constraints for S-shaped activation functions, which is better than both existing Neuron-wise Tightest and Network-wise Tightest tools. We evaluate it with 48 different CNNs trained on MNIST, CIFAR-10, and Tiny ImageNet datasets.
• Score: 5.678314425261842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The robustness of neural network classifiers is becoming important in the safety-critical domain and can be quantified by robustness verification. However, at present, efficient and scalable verification techniques are always sound but incomplete. Therefore, the improvement of certified robustness bounds is the key criterion to evaluate the superiority of robustness verification approaches. In this paper, we present a Tight Linear approximation approach for robustness verification of Convolutional Neural Networks(Ti-Lin). For general CNNs, we first provide a new linear constraints for S-shaped activation functions, which is better than both existing Neuron-wise Tightest and Network-wise Tightest tools. We then propose Neuron-wise Tightest linear bounds for Maxpool function. We implement Ti-Lin, the resulting verification method. We evaluate it with 48 different CNNs trained on MNIST, CIFAR-10, and Tiny ImageNet datasets. Experimental results show that Ti-Lin significantly outperforms other five state-of-the-art methods(CNN-Cert, DeepPoly, DeepCert, VeriNet, Newise). Concretely, Ti-Lin certifies much more precise robustness bounds on pure CNNs with Sigmoid/Tanh/Arctan functions and CNNs with Maxpooling function with at most 63.70% and 253.54% improvement, respectively.

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