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Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming.
Fault sneaking attack: A stealthy framework for misleading deep neural networks
Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, and Xue Lin · 1908
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The mnist database of handwritten digits
Yann LeCun · 1998
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Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance
Maciej A Mazurowski, Piotr A Habas, Jacek M Zurada, Joseph Y Lo, Jay A Baker, and Georgia D Tourassi · 2008
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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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Convolutional neural network architectures for matching natural language sentences
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
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Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 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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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Stochastic activation pruning for robust adversarial defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Jeremy D. Bernstein, Jean Kossaifi, Aran Khanna, Zachary C. Lipton, and Animashree Anandkumar · 2018
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Adversarial reprogramming of neural networks
Gamaleldin F Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
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Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 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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Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Defensive dropout for hardening deep neural networks under adversarial attacks
Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, and Xue Lin · 2018
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Using deep learning to extract scenery information in real time spatiotemporal compressed sensing
Xiao Wang, Jie Zhang, Tao Xiong, Trac Duy Tran, Sang Peter Chin, and Ralph Etienne-Cummings · 2018
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An admm-based universal framework for adversarial attacks on deep neural networks
Pu Zhao, Sijia Liu, Yanzhi Wang, and Xue Lin · 2018
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