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In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
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Weilin Xu, David Evans, and Yanjun Qi · 2017
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Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian Goodfellow · 2018
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Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
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Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, and James Storer · 2018
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Adversarial example defense: Ensembles of weak defenses are not strong
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, and Dawn Song
Cited in the paper.
Barrage of random transforms for adversarially robust defense
Edward Raff, Jared Sylvester, Steven Forsyth, and Mark McLean · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Adversarial defense by stratified convolutional sparse coding
Bo Sun, Nian-Hsuan Tsai, Fangchen Liu, Ronald Yu, and Hao Su · 2019
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Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
Jonas Rauber, Roland Zimmermann, Matthias Bethge, and Wieland Brendel · 2020
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Enhancing robustness verification for deep neural networks via symbolic propagation
Pengfei Yang, Jianlin Li, Jiangchao Liu, Cheng-Chao Huang, Renjue Li, Liqian Chen, Xiaowei Huang, and Lijun Zhang · 2021
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