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Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence.
Double backpropagation increasing generalization performance
H. Drucker and Y. Le Cun · 1992
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Simplifying neural nets by discovering flat minima
S. Hochreiter and J. Schmidhuber · 1995
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Learning with Kernels
B. Schölkopf and A. J. Smola · 2002
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Adversarial classification
N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma · 2004
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Adversarial learning
D. Lowd and C. Meek · 2005
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 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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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Towards deep neural network architectures robust to adversarial examples
S. Gu and L. Rigazio · 2015
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On the inductive bias of dropout
D. P. Helmbold and P. Long · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. J. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Józefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. G. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. A. Tucker, V. Vanhoucke, V. Vasudevan, F. B. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
Cited alongside, same era.
Measuring neural net robustness with constraints
O. Bastani, Y. Ioannou, L. Lampropoulos, D. Vytiniotis, A. Nori, and A. Criminisi · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of neural nets through robust optimization
U. Shaham, Y. Yamada, and S. Negahban · 2016
Later among the works it cites.
Improving the robustness of deep neural networks via stability training
S. Zheng, Y. Song, T. Leung, and I. J. Goodfellow · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
Closest in time.
Parseval networks: Improving robustness to adversarial examples
M. Cisse, P. Bojanowksi, E. Grave, Y. Dauphin, and N. Usunier · 2017
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Adversarial examples for generative models
J. Kos, I. Fischer, and D. Song · 2017
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Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2017
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Learning with a strong adversary
R. Huang, B. Xu, D. Schuurmans, and C. Szepesvari · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep networks
N. Papernot, P. McDonald, X. Wu, S. Jha, and A. Swami · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
P. Frossard S.-M. Moosavi-Dezfooli, A. Fawzi · 2016
Cited alongside, same era.
Wide residual networks
S. Zagoruyko and N. Komodakis
Cited in the paper.
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Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2017
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Universal adversarial perturbations
S.M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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