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Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning.
Learning from noisy examples
Dana Angluin and Philip D. Laird · 1988
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
A study of the effect of jpg compression on adversarial images
Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M. Roy · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Biologically inspired protection of deep networks from adversarial attacks
Aran Nayebi and Surya Ganguli · 2017
Cited alongside, same era.
No bot expects the deepcaptcha! introducing immutable adversarial examples, with applications to captcha generation
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
Later among the works it cites.
Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin A. Raffel, and Ian J. Goodfellow · 2018
Later among the works it cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Later among the works it cites.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten · 2018
Later among the works it cites.
Adversarial logit pairing
Harini Kannan, Alexey Kurakin, and Ian J. Goodfellow · 2018
Later among the works it cites.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, and Xiaolin Hu · 2018
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Margarita Osadchy, Julio Hernandez-Castro, Stuart J. Gibson, Orr Dunkelman, and Daniel Pérez-Cabo · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
Cited alongside, same era.
Adversarial examples for semantic segmentation and object detection
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan L. Yuille · 2017
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio
Cited in the paper.
Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio
Cited in the paper.
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Later among the works it cites.
Deflecting adversarial attacks with pixel deflection
Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, and James A. Storer · 2018
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, and Patrick D. McDaniel · 2018
Later among the works it cites.
The odds are odd: A statistical test for detecting adversarial examples
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
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