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Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can drastically impair the network's performance.
Improving generalization performance using double backpropagation
Harris Drucker and Yann Le Cun · 1992
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Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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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, Geoffrey Hinton, et al · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Intriguing properties of neural networks
Christian Szegedy, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Joan Bruna, Rob Fergus, and Dumitru Erhan · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
Seyed Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Tensor product generation networks for deep NLP modeling
Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, and Dapeng Wu · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Foolbox: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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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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Boosting Adversarial Attacks with Momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Ensemble Adversarial Training: Attacks and Defenses
Ian Goodfellow, Dan Boneh, and Patrick Mcdaniel · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Stochastic adversarial video prediction
Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Improved network robustness with adversary critic
Alexander Matyasko and Lap-Pui Chau · 2018
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Spectral normalization for generative adversarial networks
Improving the generalization of adversarial training with domain adaptation
Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft · 2019
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A direct approach to robust deep learning using adversarial networks
Huaxia Wang and Chun-Nam Yu · 2019
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Revisiting Loss Landscape for Adversarial Robustness
Dongxian Wu, Yisen Wang, and Xia Shu-Tao · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Towards Stable and Efficient Training of Verifiably Robust Neural Networks
Huan Zhang, Hongge Chen, Chaowei Xiao, Sven Gowal, Robert Stanforth, Bo Li, Duane Boning, and Cho-Jui Hsieh · 2019
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Takeru Miyato, Toshiki Kataoka, Koyama Masanori, and Yoshida Yuichi · 2018
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cgans with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
Cited alongside, same era.
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Takeru Miyato, Shin Ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
Cited alongside, same era.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ros and Finale Doshi-Velez · 2018
Cited alongside, same era.
Towards the first adversarially robust neural network model on mnist
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2018
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and J. Zico Kolter · 2019
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What it thinks is important is important: Robustness transfers through input gradients
Alvin Chan, Yi Tay, and Yew-Soon Ong · 2020
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Jacobian Adversarially Regularized Networks for Robustness
Alvin Chan, Yi Tay, Yew Soon Ong, and Jie Fu · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong, Zhijie Deng, Tianyu Pang, Hang Su, and Jun Zhu · 2020
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Adversarial continual learning
Sayna Ebrahimi, Franziska Meier, Roberto Calandra, Trevor Darrell, and Marcus Rohrbach · 2020
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
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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 · 2020
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Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Improving adversarial robustness through progressive hardening
Chawin Sitawarin, Supriyo Chakraborty, and David Wagner · 2020
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Robust local features for improving the generalization of adversarial training
Chuanbiao Song, He Kun, Lin Jiadong, John E Hopcroft, and Liwei Wang · 2020
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Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, and R. Venkatesh Babu · 2020
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang · 2020
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Learning adversarially robust representations via worst-case mutual information maximization
Sicheng Zhu, Xiao Zhang, and David Evans · 2020
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Improving Adversarial Robustness via Channel-wise Activation Suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang · 2021
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Geometry-aware instance-reweighted adversarial training
Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, and Mohan Kankanhalli · 2021
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