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Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art machine learning models.
Gradient-based learning applied to document recognition
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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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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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
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Intriguing properties of neural networks
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Explaining and harnessing adversarial examples
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Explaining and harnessing adversarial examples (2014)
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Deep residual learning for image recognition
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Deep residual learning for image recognition
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Simple black-box adversarial perturbations for deep networks
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Adversarial examples and adversarial training. stanford cs231n lecture16 slides
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Adversarial examples are not bugs, they are features
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Adversarial training can hurt generalization
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Are labels required for improving adversarial robustness?
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Feature denoising for improving adversarial robustness
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Language models are few-shot learners
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