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We report experimental results indicating that defensive distillation successfully mitigates adversarial samples crafted using the fast gradient sign method, in addition to those crafted using the Jacobian-based iterative attack on which the defense mechanism was originally evaluated.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The mnist database of handwritten digits, 1998
Y. LeCun and C. Cortes · 1998
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Evasion attacks against machine learning at test time
B. Biggio, I. Corona, and al · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, , et al · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, and al · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. McDaniel, and I. Goodfellow · 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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Adversarial perturbations of deep neural networks
D. Warde-Farley and I. Goodfellow · 2016
Closest in time.
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