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Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier.
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Evasion attacks against machine learning at test time. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases
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DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard. 2015 · 2015
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Evaluation of Defensive Methods for DNNs against Multiple Adversarial Evasion Models
Xinyun Chen, Bo Li, and Yevgeniy Vorobeychik. 2016 · 2016
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Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
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A General Retraining Framework for Scalable Adversarial Classification
Bo Li, Yevgeniy Vorobeychik, and Xinyun Chen. 2016 · 2016
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Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise
Beilun Wang, Ji Gao, and Yanjun Qi. 2016 · 2016
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Adversarial Transformation Networks: Learning to Generate Adversarial Examples
Shumeet Baluja and Ian Fischer. 2017 · 2017
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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Nicholas Carlini and David Wagner. 2017a · 2017
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Towards evaluating the robustness of neural networks. In Security and Privacy (SP), 2017 IEEE Symposium on
Nicholas Carlini and David Wagner. 2017b · 2017
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Xin Li and Fuxin Li. 2016 · 2016
Cited alongside, same era.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016a · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2016b · 2016
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The limitations of deep learning in adversarial settings. In Security and Privacy (EuroS&P), 2016 IEEE European Symposium on
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami. 2016c · 2016
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Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman. 2016d · 2016
Cited alongside, same era.
Generative adversarial nets. In Advances in neural information processing systems
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014a
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Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. 2017 · 2017
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On the (Statistical) Detection of Adversarial Examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2017 · 2017
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Universal adversarial perturbations. In Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017 · 2017
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, and Yanjun Qi. 2017 · 2017
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Feature cross-substitution in adversarial classification. In Advances in neural information processing systems
Bo Li and Yevgeniy Vorobeychik. 2014 · 2095
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