DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
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 pr...
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| Vydáno v: | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 2574 - 2582 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.06.2016
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| ISSN: | 1063-6919 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Moosavi-Dezfooli, Seyed-Mohsen Fawzi, Alhussein Frossard, Pascal |
| Author_xml | – sequence: 1 givenname: Seyed-Mohsen surname: Moosavi-Dezfooli fullname: Moosavi-Dezfooli, Seyed-Mohsen email: seyed.moosavi@epfl.ch organization: Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland – sequence: 2 givenname: Alhussein surname: Fawzi fullname: Fawzi, Alhussein email: alhussein.fawzi@epfl.ch organization: Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland – sequence: 3 givenname: Pascal surname: Frossard fullname: Frossard, Pascal email: pascal.frossard@epfl.ch organization: Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland |
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| Snippet | State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to... |
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| SubjectTerms | Algorithm design and analysis Computer vision Level set Neural networks Optimization Pattern recognition Robustness |
| Title | DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks |
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