title: Boosting Robustness Certification of Neural Networks
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.
authors: Gagandeep Singh Timon Gehr Markus Püschel Martin Vechev
hIndex: Martin Vechev 45