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Adversarial examples have become one of the largest challenges that machine learning models, especially neural network classifiers, face.
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Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, and Tom Goldstein. 2019 · 1904
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
Earlier work this paper cites.
Integrating structured biological data by kernel maximum mean discrepancy
Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola. 2006 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks. In Security and Privacy (SP), 2016 IEEE Symposium on . IEEE, 582–597
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016 · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 39–57
Nicholas Carlini and David Wagner. 2017 · 2017
Cited alongside, same era.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017 · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner. 2018 · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow. 2018 · 2018
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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, and Rujun Long. 2018 · 2018
Later among the works it cites.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa. 2018 · 2018
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Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 135–147
Dongyu Meng and Hao Chen. 2017 · 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 · 2017
Cited alongside, same era.
Improving the Generalization of Adversarial Training with Domain Adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. 2018 · 2018
Later among the works it cites.
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Guanxiong Liu, Issa Khalil, and Abdallah Khreishah. 2019 · 2019
Later among the works it cites.