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We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization.
The mnist database of handwritten digits
Yann LeCun · 1998
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Adversarial classification
Nilesh Dalvi, Pedro Domingos, Sumit Sanghai, and Deepak Verma · 2004
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Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 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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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Convex adversarial collective classification
MohamadAli Torkamani and Daniel Lowd · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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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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On robustness and regularization of structural support vector machines
Mohamad Ali Torkamani and Daniel Lowd · 2014
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Manitest: Are classifiers really invariant?
Alhussein Fawzi and Pascal Frossard · 2015
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Adversarial examples
Ian Goodfellow · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 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, Alexander C. Berg, and Li Fei-Fei · 2015
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What makes tom hanks look like tom hanks
Supasorn Suwajanakorn, Steven M Seitz, and Ira Kemelmacher-Shlizerman · 2015
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Transfiguring portraits
Adversarial spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
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Lessons learned from manually classifying cifar-10
Andrej Karpathy · 2018
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What I learned from competing against a ConvNet on ImageNet
Andrej Karpathy · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Ira Kemelmacher-Shlizerman · 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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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Tensorpack
Yuxin Wu et al · 2016
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Xavier Gastaldi · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Mądry · 2018
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Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Is robustness the cost of accuracy? a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2018
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Analyzing the robustness of nearest neighbors to adversarial examples
Yizhen Wang, Somesh Jha, and Kamalika Chaudhuri · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J Zico Kolter · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and Zico Kolter · 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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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Xiao, and Russ Tedrake · 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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