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Evaluating robustness of machine-learning models to adversarial examples is a challenging problem.
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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A fast griffin-lim algorithm
N. Perraudin, P. Balazs, and P. L. Søndergaard · 2013
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Drebin: Efficient and explainable detection of android malware in your pocket
D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck · 2014
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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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Defensive distillation is not robust to adversarial examples, 2016
N. Carlini and D. Wagner · 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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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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MagNet: a two-pronged defense against adversarial examples
D. Meng and H. Chen · 2017
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Thermometer encoding: One hot way to resist adversarial examples
J. Buckman, A. Roy, C. Raffel, and I. Goodfellow · 2018
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Shield: Fast, practical defense and vaccination for deep learning using jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
Earlier work this paper cites.
Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cisse, and L. van der Maaten · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
Earlier work this paper cites.
Malware detection by eating a whole EXE
E. Raff, J. Barker, J. Sylvester, R. Brandon, B. Catanzaro, and C. K. Nicholas · 2018
Earlier work this paper cites.
Adversarial examples: Attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, and X. Li · 2018
Cited alongside, same era.
Accurate, reliable and fast robustness evaluation, 2019
W. Brendel, J. Rauber, M. Kümmerer, I. Ustyuzhaninov, and M. Bethge · 2019
Cited alongside, same era.
A critique of the deepsec platform for security analysis of deep learning models, 2019
N. Carlini · 2019
Cited alongside, same era.
On evaluating adversarial robustness, 2019
N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. Goodfellow, A. Madry, and A. Kurakin · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Y. Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang · 2019
Cited alongside, same era.
Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks
A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio, A. Oprea, C. Nita-Rotaru, and F. Roli · 2019
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F. Croce and M. Hein · 2020
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Hydra: Pruning adversarially robust neural networks
V. Sehwag, S. Wang, P. Mittal, and S. Jana · 2020
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Deep neural rejection against adversarial examples
A. Sotgiu, A. Demontis, M. Melis, B. Biggio, G. Fumera, X. Feng, and F. Roli · 2020
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Confidence-calibrated adversarial training: Generalizing to unseen attacks
D. Stutz, M. Hein, and B. Schiele · 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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Adversarial weight perturbation helps robust generalization
D. Wu, S.-T. Xia, and Y. Wang · 2020
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Cited alongside, same era.
Mma training: Direct input space margin maximization through adversarial training
G. W. Ding, Y. Sharma, K. Y. C. Lui, and R. Huang · 2019
Cited alongside, same era.
Robustness (python library), 2019
L. Engstrom, A. Ilyas, H. Salman, S. Santurkar, and D. Tsipras · 2019
Cited alongside, same era.
Defending against neural network model stealing attacks using deceptive perturbations
T. Lee, B. Edwards, I. Molloy, and D. Su · 2019
Cited alongside, same era.
Deepsec: A uniform platform for security analysis of deep learning model
X. Ling, S. Ji, J. Zou, J. Wang, C. Wu, B. Li, and T. Wang · 2019
Cited alongside, same era.
Improving adversarial robustness via promoting ensemble diversity
T. Pang, K. Xu, C. Du, N. Chen, and J. Zhu · 2019
Cited alongside, same era.
The odds are odd: A statistical test for detecting adversarial examples
K. Roth, Y. Kilcher, and T. Hofmann · 2019
Cited alongside, same era.
Later among the works it cites.
Resisting adversarial attacks by k k -winners-take-all
C. Xiao, P. Zhong, and C. Zheng · 2020
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Robustbench: a standardized adversarial robustness benchmark
F. Croce, M. Andriushchenko, V. Sehwag, E. Debenedetti, N. Flammarion, M. Chiang, P. Mittal, and M. Hein · 2021
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Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection
L. Demetrio, S. E. Coull, B. Biggio, G. Lagorio, A. Armando, and F. Roli · 2021
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Data augmentation can improve robustness
S.-A. Rebuffi, S. Gowal, D. A. Calian, F. Stimberg, O. Wiles, and T. A. Mann · 2021
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Automated discovery of adaptive attacks on adversarial defenses
C. Yao, P. Bielik, P. Tsankov, and M. Vechev · 2021
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Evaluating the adversarial robustness of adaptive test-time defenses
F. Croce, S. Gowal, T. Brunner, E. Shelhamer, M. Hein, and T. Cemgil · 2022
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secml: Secure and explainable machine learning in python
M. Pintor, L. Demetrio, A. Sotgiu, M. Melis, A. Demontis, and B. Biggio · 2022
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