Task: Summarization
Input (English): 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.
Output (English): We refine the over-approximation results from incomplete verifiers using MILP solvers to prove more robustness properties than state-of-the-art.