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In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, and Alexey Kurakin · 1902
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Introduction to Fourier Analysis on Euclidean Spaces
E.M. Stein and G. Weiss · 1971
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Defensive distillation is not robust to adversarial examples
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Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, and Andrew B. Gardner · 2017
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Defense against adversarial attacks using high-level representation guided denoiser
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner
Cited in the paper.
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan L. Yuille · 2017
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Genattack: Practical black-box attacks with gradient-free optimization
Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, and Mani B. Srivastava · 2018
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On the robustness of the cvpr 2018 white-box adversarial example defenses
Anish Athalye and Nicholas Carlini · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David A. Wagner · 2018
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Deflecting adversarial attacks with pixel deflection
Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, and James A. Storer · 2018
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A new perspective on machine learning: How to do perfect supervised learning
Hui Jiang · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
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