DeepFool is a simple algorithm to find the minimum perturbations needed in deep networks to change the outcome of its decision.

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. DeepFool proposes to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers.