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Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J Goodfellow, and Rob Fergus · 2014
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Explaining and Harnessing Adversarial Examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2016
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Adversarial Machine Learning at Scale
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 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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The space of transferable adversarial examples
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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On the robustness of the cvpr 2018 white-box adversarial example defenses
Anish Athalye and Nicholas Carlini · 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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Curriculum Adversarial Training
Qi-Zhi Cai, Chang Liu, and Dawn Song · 2018
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Pac-learning in the presence of evasion adversaries
Daniel Cullina, Arjun Nitin Bhagoji, and Prateek Mittal · 2018
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Evaluating and understanding the robustness of adversarial logit pairing
Logan Engstrom, Andrew Ilyas, and Anish Athalye · 2018
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Adversarial Logit Pairing
Harini Kannan, Alexey Kurakin, and Ian J Goodfellow · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
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Is robustness the cost of accuracy? - A comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J Goodfellow, Dan Boneh, and Patrick D McDaniel · 2018
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Robustness may be at odds with accuracy, 2018
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Are Labels Required for Improving Adversarial Robustness?
Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, and Pushmeet Kohli · 2019
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Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji, Tom Goldstein, and Judy Hoffman · 2019
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Unlabeled Data Improves Adversarial Robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
Cited alongside, same era.
Transfer of adversarial robustness between perturbation types
Daniel Kang, Yi Sun, Tom Brown, Dan Hendrycks, and Jacob Steinhardt · 2019
Cited alongside, same era.
Improving adversarial robustness of ensembles with diversity training
Sanjay Kariyappa and Moinuddin K Qureshi · 2019
Cited alongside, same era.
Metric Learning for Adversarial Robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, and Baishakhi Ray · 2019
Cited alongside, same era.
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato · 2019
Cited alongside, same era.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang · 2020
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Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, and Hang Su · 2020
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Bridging the performance gap between fgsm and pgd adversarial training, 2020
Tianjin Huang, Vlado Menkovski, Yulong Pei, and Mykola Pechenizkiy · 2020
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Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Saehyung Lee, Hyungyu Lee, and Sungroh Yoon · 2020
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Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini, Eric Wong, and Zico Kolter · 2020
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Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Adversarial training can hurt generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C Duchi, and Percy Liang · 2019
Cited alongside, same era.
Towards the first adversarially robust neural network model on MNIST
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2019
Cited alongside, same era.
Adversarial training for free!
Ali Shafahi, John Dickerson, Gavin Taylor, Christoph Studer, Tom Goldstein, Larry S Davis, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, Tom Goldstein, John Dickerson, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
Cited alongside, same era.
Improving the generalization of adversarial training with domain adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E. Hopcroft · 2019
Cited alongside, same era.
Disentangling Adversarial Robustness and Generalization
David Stutz, Matthias Hein, and Bernt Schiele · 2019
Cited alongside, same era.
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2020
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Boosting Adversarial Training with Hypersphere Embedding, 2020
Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Hang Su, and Jun Zhu · 2020
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Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, and Mingyi Hong · 2020
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Overfitting in adversarially robust deep learning, 2020
Leslie Rice, Eric Wong, and J. Zico Kolter · 2020
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Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
Samuel Henrique Silva and Peyman Najafirad · 2020
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Robust local features for improving the generalization of adversarial training
Chuanbiao Song, Kun He, Jiadong Lin, Liwei Wang, and John E. Hopcroft · 2020
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Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz, Matthias Hein, and Bernt Schiele · 2020
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Regularizers for single-step adversarial training, 2020
B. S. Vivek and R. Venkatesh Babu · 2020
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Single-step adversarial training with dropout scheduling
B. S. Vivek and R. Venkatesh Babu · 2020
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Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J. Zico Kolter · 2020
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Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, and Quoc V Le · 2020
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On the Generalization Properties of Adversarial Training
Yue Xing, Qifan Song, and Guang Cheng · 2020
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DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, and Hai Li · 2020
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A closer look at accuracy vs. robustness, 2020
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, and Kamalika Chaudhuri · 2020
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Adversarial interpolation training: A simple approach for improving model robustness, 2020
Haichao Zhang and Wei Xu · 2020
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Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli · 2020
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Understanding catastrophic overfitting in single-step adversarial training
Hoki Kim, Woojin Lee, and Jaewook Lee · 2021
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