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Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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The class imbalance problem: A systematic study
Nathalie Japkowicz and Shaju Stephen · 2002
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C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling
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A multiple resampling method for learning from imbalanced data sets
Andrew Estabrooks, Taeho Jo, and Nathalie Japkowicz · 2004
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What is a support vector machine?
William S Noble · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Cluster-based under-sampling approaches for imbalanced data distributions
Show-Jane Yen and Yue-Shi Lee · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Imbalanced learning: foundations, algorithms, and applications
Haibo He and Yunqian Ma · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 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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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2019
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Theoretically principled trade-off between robustness and accuracy
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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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The devil is in the tails: Fine-grained classification in the wild
Grant Van Horn and Pietro Perona · 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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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A Mazurowski · 2018
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Deeprobust: A pytorch library for adversarial attacks and defenses
Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and Zico Kolter · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, and Jiliang Tang · 2020
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Adversarial attacks and defenses in images, graphs and text: A review
Han Xu, Yao Ma, Hao-Chen Liu, Debayan Deb, Hui Liu, Ji-Liang Tang, and Anil K Jain · 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 the role of importance weighting for deep learning
Da Xu, Yuting Ye, and Chuanwei Ruan · 2021
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