Boosting Robustness Certification of Neural NetworksDownload PDF

Sep 27, 2018 (edited Apr 05, 2019)ICLR 2019 Conference Blind SubmissionReaders: Everyone
  • Abstract: We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.
  • Keywords: Robustness certification, Adversarial Attacks, Abstract Interpretation, MILP Solvers, Verification of Neural Networks
  • TL;DR: We refine the over-approximation results from incomplete verifiers using MILP solvers to prove more robustness properties than state-of-the-art.
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