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.

@incollection{singh2019refinement, title = {Boosting Robustness Certification of Neural Networks}, author = {Singh, Gagandeep and Gehr, Timon and Püschel, Markus and Vechev, Martin}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2019} }