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Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences.
Some methods of speeding up the convergence of iteration methods
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Towards evaluating the robustness of neural networks
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Y. Li and Y. Gal · 2017
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Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2017
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On detecting adversarial perturbations
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Universal adversarial perturbations
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Robust deep learning via reverse cross-entropy training and thresholding test
T. Pang, C. Du, and J. Zhu · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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Y. Dong, H. Su, J. Zhu, and F. Bao · 2017
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A. Kurakin, I. Goodfellow, and S. Bengio · 2017
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