Accurate, reliable and fast robustness evaluation

NeurIPS 2019 Wieland BrendelJonas RauberMatthias KümmererIvan UstyuzhaninovMatthias Bethge

Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. Despite much attention, however, progress towards more robust models is significantly impaired by the difficulty of evaluating the robustness of neural network models... (read more)

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