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Adversarial examples are perturbed inputs designed to fool machine learning models.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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CRC handbook of combinatorial designs
Charles J Colbourn · 2010
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Generalization bounds for domain adaptation
Chao Zhang, Lei Zhang, and Jieping Ye · 2012
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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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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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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Foveation-based mechanisms alleviate adversarial examples
Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, and Qi Zhao · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 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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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, 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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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Adversarial transformation networks: Learning to generate adversarial examples
Shumeet Baluja and Ian Fischer · 2017
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Comment on” biologically inspired protection of deep networks from adversarial attacks”
Wieland Brendel and Matthias Bethge · 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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Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
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The space of transferable adversarial examples
Florian Tramèr, 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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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 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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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Bojanowski Piotr, Grave Edouard, Dauphin Yann, and Usunier Nicolas · 2017
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A rotation and a translation suffice: Fooling cnns with simple transformations
Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 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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Nips 2017: Defense against adversarial attack, 2017c
Alexey Kurakin, Ian J Goodfellow, and Samy Bengio · 2017
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Delving into transferable adversarial examples and black-box attacks
Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song · 2017
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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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Systematic evaluation of convolution neural network advances on the imagenet
Dmytro Mishkin, Nikolay Sergievskiy, and Jiri Matas · 2017
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Generating adversarial examples with adversarial networks, 2018
Chaowei Xiao, Bo Li, Jun-Yan Zhu, 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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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and 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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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
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Skip connections matter: On the transferability of adversarial examples generated with resnets
Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, and Xingjun Ma · 2020
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