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Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations.
Cost-sensitive learning with neural networks
Matjaž Kukar and Igor Kononenko · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Metacost: A general method for making classifiers cost-sensitive
Pedro Domingos · 1999
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The foundations of cost-sensitive learning
Charles Elkan · 2001
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Cost-sensitive learning by cost-proportionate example weighting
Bianca Zadrozny, John Langford, and Naoki Abe · 2003
Earlier work this paper cites.
Adversarial classification
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, Deepak Verma, et al · 2004
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The influence of class imbalance on cost-sensitive learning: An empirical study
Xu-Ying Liu and Zhi-Hua Zhou · 2006
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
On multi-class cost-sensitive learning
Zhi-Hua Zhou and Xu-Ying Liu · 2010
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
Earlier work this paper cites.
Adversarial cost-sensitive classification
Kaiser Asif, Wei Xing, Sima Behpour, and Brian D Ziebart · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Deep face recognition
Omkar M Parkhi, Andrea Vedaldi, and Andrew Zisserman · 2015
Cited alongside, same era.
Deep neural network based malware detection using two dimensional binary program features
Joshua Saxe and Konstantin Berlin · 2015
Cited alongside, same era.
End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
Semantic adversarial deep learning
Tommaso Dreossi, Somesh Jha, and Sanjit A Seshia · 2018
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Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2018
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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On the suitability of l p l_{p} -norms for creating and preventing adversarial examples
Mahmood Sharif, Lujo Bauer, and Michael K Reiter · 2018
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
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Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
A rotation and a translation suffice: Fooling CNNs with simple transformations
Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
Cited alongside, same era.
Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Closest in time.
Formal security analysis of neural networks using symbolic intervals
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, and Suman Jana · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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The curse of concentration in robust learning: Evasion and poisoning attacks from concentration of measure
Saeed Mahloujifar, Dimitrios I Diochnos, and Mohammad Mahmoody · 2019
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