Fetching the paper…
Reading the bibliography…
We study the problem of learning classifiers robust to universal adversarial perturbations.
Iterative solution of games by fictitious play
George W Brown · 1951
Earlier work this paper cites.
Statistical learning theory. 1998
Vladimir Vapnik · 1998
Earlier work this paper cites.
Theory of games and economic behavior (commemorative edition)
John Von Neumann and Oskar Morgenstern · 2000
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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 Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Long-term recurrent convolutional networks for visual recognition and description
Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
Earlier work this paper cites.
Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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 Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deep reinforcement learning from self-play in imperfect-information games
Johannes Heinrich and David Silver · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
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.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, and Xiaolin Hu · 2017
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 · 2017
Later among the works it cites.
Ask your neurons: A deep learning approach to visual question answering
Mateusz Malinowski, Marcus Rohrbach, and Mario Fritz · 2017
Later among the works it cites.
Universal adversarial perturbations against semantic image segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox, and Volker Fischer · 2017
Later among the works it cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilya, and Kevin Kwok · 2017
Cited alongside, same era.
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten · 2017
Cited alongside, same era.
A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, and Thore Graepel · 2017
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman
Cited in the paper.
Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
Later among the works it cites.
Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2017
Later among the works it cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Closest in time.
Actor-critic fictitious play in simultaneous move multistage games
Julien Pérolat, Bilal Piot, and Olivier Pietquin · 2018
Closest in time.