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The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Image denoising and inpainting with deep neural networks
Junyuan Xie, Linli Xu, and Enhong Chen · 2012
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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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Rényi divergence and kullback-leibler divergence
Tim Van Erven and Peter Harremos · 2014
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Identity mappings in deep residual networks
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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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard · 2016
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Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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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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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 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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Formal guarantees on the robustness of a classifier against adversarial manipulation
Matthias Hein and Maksym Andriushchenko · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 2017
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Variance-based regularization with convex objectives
Hongseok Namkoong and John C Duchi · 2017
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2017
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Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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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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Efficient defenses against adversarial attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
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Magnet and" efficient defenses against adversarial attacks" are not robust to adversarial examples
Nicholas Carlini and David Wagner · 2017
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Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang · 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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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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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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T. B. Brown, N. Carlini, C. Zhang, C. Olsson, P. Christiano, and I. Goodfellow · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 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 examples are a natural consequence of test error in noise
Nic Ford, Justin Gilmer, Nicolas Carlini, and Dogus Cubuk · 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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On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, and Aleksander Madry · 2019
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On norm-agnostic robustness of adversarial training, 2019
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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$\ell_1$ adversarial robustness certificates: a randomized smoothing approach
Anonymous · 2020
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