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Deep Neural Networks are quite vulnerable to adversarial perturbations.
The conjugate gradient method and trust regions in large scale optimization
Trond Steihaug · 1983
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Numerical Optimization
J. Nocedal and S. Wright · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 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
Cited alongside, same era.
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.
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 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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Regularizing deep networks using efficient layerwise adversarial training
Swami Sankaranarayanan, Arpit Jain, Rama Chellappa, and Ser Nam Lim · 2017
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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https://github.com/amirgholami/trattack, November 2018
2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
On detecting adversarial perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff · 2017
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Cited alongside, same era.
Swish: a self-gated activation function
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban · 2018
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Large batch size training of neural networks with adversarial training and second-order information
Zhewei Yao, Amir Gholami, Kurt Keutzer, and Michael W. Mahoney · 2018
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Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 2018
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