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Dataset bias is a problem in adversarial machine learning, especially in the evaluation of defenses.
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Deep residual learning for image recognition
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Simple black-box adversarial perturbations for deep networks
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
Arjun Nitin Bhagoji, Daniel Cullina, and Prateek Mittal · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Li Chen, Michael E Kounavis, and Duen Horng Chau · 2017
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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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Gavin Weiguang Ding, Luyu Wang, and Xiaomeng Jin · 2019
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Jumprelu: A retrofit defense strategy for adversarial attacks
N Benjamin Erichson, Zhewei Yao, and Michael W Mahoney · 2019
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Natural adversarial examples, 2019
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Practical black-box attacks against machine learning
Nicolas Papernot et al · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Glcm and its application in pattern recognition
S. Singh, D. Srivastava, and S. Agarwal · 2017
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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Defense against universal adversarial perturbations
Naveed Akhtar, Jian Liu, and Ajmal Mian · 2018
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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Rob-gan: Generator, discriminator, and adversarial attacker
Xuanqing Liu and Cho-Jui Hsieh · 2019
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Barrage of random transforms for adversarially robust defense
Edward Raff, Jared Sylvester, Steven Forsyth, and Mark McLean · 2019
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Jerome Rony, Luiz G. Hafemann, Luiz S. Oliveira, Ismail Ben Ayed, Robert Sabourin, and Eric Granger · 2019
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Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses
Jérôme Rony, Luiz G Hafemann, Luiz S Oliveira, Ismail Ben Ayed, Robert Sabourin, and Eric Granger · 2019
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Disentangling adversarial robustness and generalization
David Stutz, Matthias Hein, and Bernt Schiele · 2019
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A kernelized manifold mapping to diminish the effect of adversarial perturbations
Saeid Asgari Taghanaki, Kumar Abhishek, Shekoofeh Azizi, and Ghassan Hamarneh · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
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Adversarial perturbations prevail in the y-channel of the ycbcr color space, 2020
Camilo Pestana, Naveed Akhtar, Wei Liu, David Glance, and Ajmal Mian · 2020
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