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Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation.
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Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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End-to-end training of deep visuomotor policies
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Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Diverse and accurate image description using a variational auto-encoder with an additive gaussian encoding space
Liwei Wang, Alexander Schwing, and Svetlana Lazebnik · 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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Tom B Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul Christiano, and Ian Goodfellow · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Semantic adversarial examples
Hossein Hosseini and Radha Poovendran · 2018
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Constructing unrestricted adversarial examples with generative models
Yang Song et al · 2018
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Generating realistic unrestricted adversarial inputs using dual-objective gan training
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Semantic adversarial attacks: Parametric transformations that fool deep classifiers
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