DeepFool: a simple and accurate method to fool deep neural networks

CVPR 2016 Seyed-Mohsen Moosavi-Dezfooli • Alhussein Fawzi • Pascal Frossard

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this phenomenon, no effective methods have been proposed to accurately compute the robustness of state-of-the-art deep classifiers to such perturbations on large-scale datasets.

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