  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. In this paper, we
fill this gap and propose the DeepFool algorithm to efficiently compute
perturbations that fool deep networks, and thus reliably quantify the
robustness of these classifiers. Extensive experimental results show that our
approach outperforms recent methods in the task of computing adversarial
perturbations and making classifiers more robust.
