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

被引：3143

作者：

Moosavi-Dezfooli, Seyed-Mohsen ^{[1
]}

Fawzi, Alhussein ^{[1
]}

Frossard, Pascal ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, Lausanne, Switzerland

来源：

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2016年

关键词：

D O I：

10.1109/CVPR.2016.282

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.(1)

引用

页码：2574 / 2582

页数：9