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Deep networks have achieved impressive results across a variety of important tasks.
Regularized auto-encoders estimate local statistics
Alain, Guillaume, Bengio, Yoshua, and Rifai, Salah · 2012
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Better mixing via deep representations
Bengio, Yoshua, Mesnil, Grégoire, Dauphin, Yann, and Rifai, Salah · 2012
Earlier work this paper cites.
Better mixing via deep representations
Bengio, Yoshua, Mesnil, Grégoire, Dauphin, Yann, and Rifai, Salah · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Gu, Shixiang and Rigazio, Luca · 2014
Earlier work this paper cites.
Deep learning
LeCun, Yann, Bengio, Yoshua, and Hinton, Geoffrey · 2015
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, Nicolas, McDaniel, Patrick D., Wu, Xi, Jha, Somesh, and Swami, Ananthram · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Bojarski, Mariusz, Del Testa, Davide, Dworakowski, Daniel, Firner, Bernhard, Flepp, Beat, Goyal, Prasoon, Jackel, Lawrence D, Monfort, Mathew, Muller, Urs, Zhang, Jiakai, et al · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, Alexey, Goodfellow, Ian J., and Bengio, Samy · 2016
Cited alongside, same era.
Improving generative adversarial networks with denoising feature matching
Warde-Farley, David and Bengio, Yoshua · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Athalye, Anish, Engstrom, Logan, Ilyas, Andrew, and Kwok, Kevin · 2017
Cited alongside, same era.
Adversarial Patch
Brown, T. B., Mané, D., Roy, A., Abadi, M., and Gilmer, J · 2017
Cited alongside, same era.
The Robust Manifold Defense: Adversarial Training using Generative Models
Ilyas, A., Jalal, A., Asteri, E., Daskalakis, C., and Dimakis, A. G · 2017
Cited alongside, same era.
Adversarial generative nets: Neural network attacks on state-of-the-art face recognition
Sharif, Mahmood, Bhagavatula, Sruti, Bauer, Lujo, and Reiter, Michael K · 2017
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, Weilin, Evans, David, and Qi, Yanjun · 2017
Later among the works it cites.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Closest in time.
Generating adversarial examples with adversarial networks, 2018
Chaowei Xiao, Bo Li, Jun-Yan Zhu Warren He Mingyan Liu Dawn Song · 2018
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A3t: Adversarially augmented adversarial training
Erraqabi, Akram, Baratin, Aristide, Bengio, Yoshua, and Lacoste-Julien, Simon · 2018
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Liao, F., Liang, M., Dong, Y., Pang, T., Zhu, J., and Hu, X · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
cleverhans v2.0.0: an adversarial machine learning library
Nicolas Papernot, Nicholas Carlini, Ian Goodfellow Reuben Feinman Fartash Faghri Alexander Matyasko Karen Hambardzumyan Yi-Lin Juang Alexey Kurakin Ryan Sheatsley Abhibhav Garg Yen-Chen Lin · 2017
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal
Cited in the paper.
Practical black-box attacks against deep learning systems using adversarial examples
Papernot, Nicolas, McDaniel, Patrick D., Goodfellow, Ian J., Jha, Somesh, Celik, Z. Berkay, and Swami, Ananthram
Cited in the paper.
Towards the science of security and privacy in machine learning
Papernot, Nicolas, McDaniel, Patrick D., Sinha, Arunesh, and Wellman, Michael P
Cited in the paper.
Closest in time.
Adversarial Spheres
Gilmer, J., Metz, L., Faghri, F., Schoenholz, S. S., Raghu, M., Wattenberg, M., and Goodfellow, I · 2018
Closest in time.
Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel Ian Goodfellow · 2018
Closest in time.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, Rama Chellappa · 2018
Closest in time.