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Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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 · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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A study of the effect of jpg compression on adversarial images
Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M Roy · 2016
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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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Technical report on the cleverhans v2. 1.0 adversarial examples library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, et al · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 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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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 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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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2018
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
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Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P Mohanty, and Matthias Bethge · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 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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Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens van der Maaten · 2018
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Decision boundary analysis of adversarial examples
Warren He, Bo Li, and Dawn Song · 2018
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Spatially transformed adversarial examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2018
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2018
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Deep defense: Training dnns with improved adversarial robustness
Ziang Yan, Yiwen Guo, and Changshui Zhang · 2018
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A critique of the deepsec platform for security analysis of deep learning models
Nicholas Carlini · 2019
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Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin · 2018
Cited alongside, same era.
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
Cited alongside, same era.
Adversarial attacks and defences competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, et al · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Adversarial robustness toolbox v0. 4.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M Molloy, et al · 2018
Cited alongside, same era.
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, and Aleksander Madry · 2019
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Query-efficient hard-label black-box attack: An optimization-based approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh · 2019
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Improving black-box adversarial attacks with a transfer-based prior
Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 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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Evading defenses to transferable adversarial examples by translation-invariant attacks
Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu · 2019
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Efficient decision-based black-box adversarial attacks on face recognition
Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, and Jun Zhu · 2019
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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, and Boqing Gong · 2019
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Deepsec: A uniform platform for security analysis of deep learning model
Xiang Ling, Shouling Ji, Jiaxu Zou, Jiannan Wang, Chunming Wu, Bo Li, and Ting Wang · 2019
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Adv-bnn: Improved adversarial defense through robust bayesian neural network
Xuanqing Liu, Yao Li, Chongruo Wu, and Cho-Jui Hsieh · 2019
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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Training for faster adversarial robustness verification via inducing relu stability
Kai Y Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, and Aleksander Madry · 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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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
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The limitations of adversarial training and the blind-spot attack
Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S Dhillon, and Cho-Jui Hsieh · 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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Defending against whitebox adversarial attacks via randomized discretization
Y. Zhang and P. Liang · 2019
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