Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-030-81685-8_1
Title: NNrepair: Constraint-Based Repair of Neural Network Classifiers
Authors: Usman, Muhammad
Gopinath, Divya
Sun, Youcheng
Noller, Yannic 
Păsăreanu, C.S.
Issue Date: 1-Jan-2021
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Usman, Muhammad, Gopinath, Divya, Sun, Youcheng, Noller, Yannic, Păsăreanu, C.S. (2021-01-01). NNrepair: Constraint-Based Repair of Neural Network Classifiers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12759 LNCS : Mar-25. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-030-81685-8_1
Rights: Attribution 4.0 International
Abstract: We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer. NNrepair first uses fault localization to find potentially faulty network parameters (such as the weights) and then performs repair using constraint solving to apply small modifications to the parameters to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the overall accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNrepair can improve the accuracy by 45.56% points on poisoned data and 10.40% points on adversarial data. NNrepair also provides small improvement in the overall accuracy of models, without requiring new data or re-training. © 2021, The Author(s).
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: https://scholarbank.nus.edu.sg/handle/10635/232249
ISSN: 0302-9743
DOI: 10.1007/978-3-030-81685-8_1
Rights: Attribution 4.0 International
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