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While great progress has been made at making neural networks effective across a wide range of visual tasks, most models are surprisingly vulnerable.
Adversarial classification
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A. Krizhevsky and G. Hinton · 2009
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Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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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. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Imagenet large scale visual recognition challenge
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
G. Pereyra, G. Tucker, J. Chorowski, Ł. Kaiser, and G. Hinton · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
Cited alongside, same era.
Evaluating and understanding the robustness of adversarial logit pairing
L. Engstrom, A. Ilyas, and A. Athalye · 2018
Cited alongside, same era.
Technical report on the cleverhans v2.1.0 adversarial examples library
N. Papernot, F. Faghri, N. Carlini, I. Goodfellow, R. Feinman, A. Kurakin, C. Xie, Y. Sharma, T. Brown, A. Roy, A. Matyasko, V. Behzadan, K. Hambardzumyan, Z. Zhang, Y.-L. Juang, Z. Li, R. Sheatsley, A. Garg, J. Uesato, W. Gierke, Y. Dong, D. Berthelot, P. Hendricks, J. Rauber, and R. Long · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
Later among the works it cites.
Adversarially robust generalization requires more data
L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, and A. Madry · 2018
Later among the works it cites.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman · 2018
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Between-class learning for image classification
Y. Tokozume, Y. Ushiku, and T. Harada · 2018
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Robust physical-world attacks on deep learning visual classification
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song · 2018
Cited alongside, same era.
Motivating the rules of the game for adversarial example research
J. Gilmer, R. P. Adams, I. Goodfellow, D. Andersen, and G. E. Dahl · 2018
Cited alongside, same era.
Data augmentation by pairing samples for images classification
H. Inoue · 2018
Cited alongside, same era.
H. Kannan, A. Kurakin, and I. Goodfellow · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
Cited alongside, same era.
Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O’Donoghue, A. v. d. Oord, and P. Kohli · 2018
Later among the works it cites.
mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2018
Later among the works it cites.
Adversarial attacks on medical machine learning
S. G. Finlayson, J. D. Bowers, J. Ito, J. L. Zittrain, A. L. Beam, and I. S. Kohane · 2019
Closest in time.
Improved mixed-example data augmentation
C. Summers and M. J. Dinneen · 2019
Closest in time.