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Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.
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Defensive distillation is not robust to adversarial examples
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Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, and Andrew B. Gardner · 2017
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Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick D. McDaniel · 2017
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Towards deep learning models resistant to adversarial attacks
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Nicolas Papernot and Patrick D. McDaniel · 2016
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David Warde-Farley and Ian Goodfellow · 2016
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Nicholas Carlini and David Wagner
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
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