René's URL Explorer Experiment


Title: RobustBench: Adversarial robustness benchmark

Mail addresses
adversarial.benchmark@gmail.com

direct link

Domain: robustbench.github.io

Links:

RobustBenchhttps://robustbench.github.io/index.html
Leaderboardshttps://robustbench.github.io#leaderboard
Paperhttps://arxiv.org/abs/2010.09670
FAQhttps://robustbench.github.io#faq
Contributehttps://robustbench.github.io#contribute
Model Zoo 🚀https://github.com/RobustBench/robustbench
more than 3'000 papershttps://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html
overestimated robustnesshttps://arxiv.org/abs/1802.00420
AutoAttackhttps://github.com/fra31/auto-attack
our paperhttps://arxiv.org/abs/2010.09670
RobustBench libraryhttps://github.com/RobustBench/robustbench
flagshttps://github.com/fra31/auto-attack/blob/master/flags_doc.md
3D Common Corruptionshttps://3dcommoncorruptions.epfl.ch
herehttps://github.com/RobustBench/robustbench#new-evaluating-robustness-of-imagenet-models-against-3d-common-corruptions-imagenet-3dcc
herehttps://robustbench.github.io/#div_imagenet_corruptions_heading
herehttps://github.com/RobustBench/robustbench#corruptions-imagenet-c--imagenet-3dcc
available modelshttps://github.com/RobustBench/robustbench#model-zoo
Colab tutorialshttps://github.com/RobustBench/robustbench#notebooks
our paperhttps://arxiv.org/abs/2010.09670
CIFAR-10 (\( \ell_\infty\))https://robustbench.github.io#div_cifar10_Linf_heading
CIFAR-10 (\( \ell_2\))https://robustbench.github.io#div_cifar10_L2_heading
CIFAR-10 (Corruptions)https://robustbench.github.io#div_cifar10_corruptions_heading
CIFAR-100 (\( \ell_\infty\))https://robustbench.github.io#div_cifar100_Linf_heading
CIFAR-100 (Corruptions)https://robustbench.github.io#div_cifar100_corruptions_heading
ImageNet (\( \ell_\infty\))https://robustbench.github.io#div_imagenet_Linf_heading
ImageNet (Corruptions: IN-C, IN-3DCC)https://robustbench.github.io#div_imagenet_corruptions_heading
AutoAttack leaderboardhttps://github.com/fra31/auto-attack
AutoAttack leaderboardhttps://github.com/fra31/auto-attack
robust-ml.orghttps://www.robust-ml.org/
robust-ml.orghttps://www.robust-ml.org/
Tramer et al., 2020https://arxiv.org/abs/2002.08347
our paperhttps://arxiv.org/abs/2010.09670
foolboxhttps://github.com/bethgelab/foolbox
cleverhanshttps://github.com/tensorflow/cleverhans
advertorchhttps://github.com/BorealisAI/advertorch
herehttps://github.com/RobustBench/robustbench#model-zoo-quick-tour
Salman et al. (2020)https://arxiv.org/abs/2007.08489
Utrera et al. (2020)https://arxiv.org/abs/2007.05869
Tsipras et al. (2018)https://arxiv.org/abs/1805.12152
Kaur et al. (2019)https://arxiv.org/abs/1910.08640
Engstrom et al. (2019)https://arxiv.org/abs/1906.00945
Tramèr et al. (2018)https://arxiv.org/abs/1811.03194
Saadatpanah et al. (2019)https://arxiv.org/abs/1906.07153
Xie et al. (2019)https://arxiv.org/abs/1911.09665
Zhu et al. (2019)https://arxiv.org/abs/1909.11764
Bochkovskiy et al. (2020)https://arxiv.org/abs/2004.10934
Xie et al. (2019)https://arxiv.org/abs/1911.09665
Kang et al. (2019)https://arxiv.org/abs/1905.01034
Zhong et al. (2020)https://arxiv.org/abs/2008.03364
Zhang et al. (2019)https://arxiv.org/abs/1906.06316
Salman et al. (2019)https://arxiv.org/abs/1902.08722
Li et al. (2020)https://arxiv.org/abs/2009.04131
herehttps://github.com/RobustBench/robustbench#how-to-contribute
Francesco Croce https://twitter.com/fra__31
https://twitter.com/fra__31
https://github.com/fra31
https://scholar.google.com/citations?user=laq9cq0AAAAJ
Maksym Andriushchenkohttps://people.epfl.ch/maksym.andriushchenko
https://people.epfl.ch/maksym.andriushchenko
https://github.com/max-andr
https://scholar.google.com/citations?user=ZNtuJYoAAAAJ
Vikash Sehwaghttps://vsehwag.github.io/
https://vsehwag.github.io/
https://github.com/VSehwag
https://scholar.google.com/citations?user=JAkeEG8AAAAJ
Edoardo Debenedettihttps://edoardo.science
https://edoardo.science
https://github.com/dedeswim
https://twitter.com/edoardo_debe
Icons from Icons8https://icons8.com/icon/100413/access

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