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A recent line of work focused on making adversarial training computationally efficient for deep learning models.
Robust statistics
Peter J. Huber · 1981
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Digital image processing (2nd edition)
Rafael C Gonzales and Richard E Woods · 2002
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Nightmare at test time: robust learning by feature deletion
Amir Globerson and Sam Roweis · 2006
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Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Solving variational inequalities with stochastic mirror-prox algorithm
Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Provably minimally-distorted adversarial examples
Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill · 2017
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
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Reluplex: an efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Wild patterns: ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2018
Cited alongside, same era.
Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
SGD on neural networks learns functions of increasing complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, and Boaz Barak · 2019
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, and Pushmeet Kohli · 2019
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Image synthesis with a single (robust) classifier
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, and Tom Goldstein · 2019
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First-order adversarial vulnerability of neural networks and input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, and David Lopez-Paz · 2019
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Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2018
Cited alongside, same era.
Towards fast computation of certified robustness for relu networks
Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit S. Dhillon, and Luca Daniel · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, and Pushmeet Kohli · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C. Duchi · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
Cited alongside, same era.
Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Xiao, and Russ Tedrake · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2019
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Yolov4: Optimal speed and accuracy of object detection
Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao · 2020
Closest in time.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and J. Zico Kolter · 2020
Closest in time.
Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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Single-step adversarial training with dropout scheduling
B. S. Vivek and R. Venkatesh Babu · 2020
Closest in time.
Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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Adversarial examples improve image recognition
Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, and Quoc V Le · 2020
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