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Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified.
Efficient projections onto the l 1-ball for learning in high dimensions
J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra · 2008
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
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EAD: Elastic-net attacks to deep neural networks via adversarial examples
P.-Y. Chen, Y. Sharma, H. Zhang, J. Yi, and C.-J. Hsieh · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Foolbox: A python toolbox to benchmark the robustness of machine learning models
J. Rauber, W. Brendel, and M. Bethge · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. A. Wagner · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
B. Biggio and F. Roli · 2018
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Boosting adversarial attacks with momentum
Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li · 2018
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Unlabeled data improves adversarial robustness
Y. Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang · 2019
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AdverTorch v0.1: An adversarial robustness toolbox based on pytorch
G. W. Ding, L. Wang, and X. Jin · 2019
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Sparsefool: a few pixels make a big difference
A. Modas, S.-M. Moosavi-Dezfooli, and P. Frossard · 2019
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
J. Rony, L. G. Hafemann, L. S. Oliveira, I. B. Ayed, R. Sabourin, and E. Granger · 2019
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Robustbench: a standardized adversarial robustness benchmark
F. Croce, M. Andriushchenko, V. Sehwag, N. Flammarion, M. Chiang, P. Mittal, and M. Hein · 2020
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J. Uesato, B. O’Donoghue, A. v. d. Oord, and P. Kohli · 2018
Cited alongside, same era.
Accurate, reliable and fast robustness evaluation
W. Brendel, J. Rauber, M. Kümmerer, I. Ustyuzhaninov, and M. Bethge · 2019
Cited alongside, same era.
On evaluating adversarial robustness
N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. Goodfellow, A. Madry, and A. Kurakin · 2019
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F. Croce and M. Hein
Cited in the paper.
Minimally distorted adversarial examples with a fast adaptive boundary attack
F. Croce and M. Hein
Cited in the paper.
Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
J. Rauber, R. Zimmermann, M. Bethge, and W. Brendel · 2020
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On adaptive attacks to adversarial example defenses
F. Tramer, N. Carlini, W. Brendel, and A. Madry · 2020
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Towards stable and efficient training of verifiably robust neural networks
H. Zhang, H. Chen, C. Xiao, S. Gowal, R. Stanforth, B. Li, D. Boning, and C.-J. Hsieh · 2020
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