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Deep neural networks have been widely deployed in various machine learning tasks.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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The shattered gradients problem: If resnets are the answer, then what is the question?
David Balduzzi, Marcus Frean, Lennox Leary, JP Lewis, Kurt Wan-Duo Ma, and Brian McWilliams · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2017
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
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Making machine learning robust against adversarial inputs
Ian Goodfellow, Patrick McDaniel, and Nicolas Papernot · 2018
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Characterizing adversarial subspaces using local intrinsic dimensionality
Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Michael E Houle, Grant Schoenebeck, Dawn Song, and James Bailey · 2018
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Cited alongside, same era.
Cascade adversarial machine learning regularized with a unified embedding
Taesik Na, Jong Hwan Ko, and Saibal Mukhopadhyay · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Later among the works it cites.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Understanding and enhancing the transferability of adversarial examples
Lei Wu, Zhanxing Zhu, Cheng Tai, et al · 2018
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