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Deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples.
A unified architecture for natural language processing: Deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
A. Krizhevsky, V. Nair, and G. Hinton · 2010
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples (2014)
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
11 adversarial perturbations of deep neural networks
D. Warde-Farley and I. Goodfellow · 2016
Cited alongside, same era.
Adversarial perturbations of deep neural networks. perturbations
D. Warde-Farley, I. Goodfellow, T. Hazan, G. Papandreou, and D. Tarlow · 2016
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel · 2017
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
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Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
F. Liao, M. Liang, Y. Dong, T. Pang, J. Zhu, and X. Hu · 2017
Cited alongside, same era.
W. Xu, D. Evans, and Y. Qi · 2017
Later among the works it cites.
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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Adversarial attacks and defences competition
A. Kurakin, I. Goodfellow, S. Bengio, Y. Dong, F. Liao, M. Liang, T. Pang, J. Zhu, X. Hu, C. Xie, et al · 2018
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Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2018
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