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The vulnerability of neural network classifiers to adversarial attacks is a major obstacle to their deployment in safety-critical applications.
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 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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Spectral norm regularization for improving the generalizability of deep learning
Yuichi Yoshida and Takeru Miyato · 2017
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
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Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2018
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Adversarial examples in the physical world
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2018
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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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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The singular values of convolutional layers
Hanie Sedghi, Vineet Gupta, and Philip M Long · 2018
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Convergence of adversarial training in overparametrized neural networks
Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, and Jason D Lee · 2019
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Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, X. Y. Han, and David L. Donoho · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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Barlow twins: Self-supervised learning via redundancy reduction
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Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2019
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A geometry-inspired decision-based attack
Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2019
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Theoretically principled trade-off between robustness and accuracy
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Robust learning with jacobian regularization
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Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning
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Recent advances in adversarial training for adversarial robustness
Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang · 2021
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Finding optimal tangent points for reducing distortions of hard-label attacks
Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, and Yisen Wang · 2021
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Neural collapse under cross-entropy loss
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A review of adversarial attack and defense for classification methods
Yao Li, Minhao Cheng, Cho-Jui Hsieh, and Thomas CM Lee · 2022
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Robust and adaptive optimization
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Cgba: Curvature-aware geometric black-box attack
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Neural networks learn to magnify areas near decision boundaries
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Model metamers reveal divergent invariances between biological and artificial neural networks
Jenelle Feather, Guillaume Leclerc, Aleksander Mądry, and Josh H. McDermott · 2023
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Neural collapse: A review on modelling principles and generalization
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A separability-based approach to quantifying generalization: which layer is best?
Luciano Dyballa, Evan Gerritz, and Steven W Zucker · 2024
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