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Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples.
Communication theory of secrecy systems
Claude E Shannon · 1949
Earlier work this paper cites.
On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
Earlier work this paper cites.
Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
Earlier work this paper cites.
The MNIST database of handwritten digits
Yann LeCun and Corrina Cortes · 1998
Earlier work this paper cites.
An intuitive justification and a simplified implementation of the map decoder for convolutional codes
Andrew J. Viterbi · 1998
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
Earlier work this paper cites.
Computer vision: algorithms and applications
Richard Szeliski · 2010
Earlier work this paper cites.
Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 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.
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
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 · 2013
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
Earlier work this paper cites.
The cifar-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
Earlier work this paper cites.
Feature cross-substitution in adversarial classification
Bo Li and Yevgeniy Vorobeychik · 2014
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
Cited alongside, same era.
Scalable optimization of randomized operational decisions in adversarial classification settings
Bo Li and Yevgeniy Vorobeychik · 2015
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2015
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Later among the works it cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Later among the works it cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Later among the works it cites.
Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Later among the works it cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Later among the works it cites.
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Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick McDaniel · 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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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li · 2016
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2017
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Adversarial example defenses: Ensembles of weak defenses are not strong
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 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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Fast feature fool: A data independent approach to universal adversarial perturbations
Konda Reddy Mopuri, Utsav Garg, and R Venkatesh Babu · 2017
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
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Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
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