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Input Validation for Neural Networks via Runtime Local Robustness Verification

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arxiv 2002.03339 v2 pith:53GEV3CQ submitted 2020-02-09 cs.LG stat.ML

Jiangchao Liu , Liqian Chen , Antoine Mine , Ji Wang This is my paper · ORCID
classification cs.LGstat.ML
keywords robustnessinputsneuraladversariallocalnetworksverificationclassified
verification ladder T0 review T1 audit T2 compute T3 formal

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Local robustness verification can verify that a neural network is robust wrt. any perturbation to a specific input within a certain distance. We call this distance Robustness Radius. We observe that the robustness radii of correctly classified inputs are much larger than that of misclassified inputs which include adversarial examples, especially those from strong adversarial attacks. Another observation is that the robustness radii of correctly classified inputs often follow a normal distribution. Based on these two observations, we propose to validate inputs for neural networks via runtime local robustness verification. Experiments show that our approach can protect neural networks from adversarial examples and improve their accuracies.

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Cited by 3 Pith papers

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