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anishathalye committed Aug 3, 2018
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#url: https://arxiv.org/abs/1711.08478
#code: https://github.com/carlini/breaking_efficient_defenses
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name: Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
url: https://arxiv.org/abs/1511.04508
authors: Papernot et al.
code: https://github.com/lengstrom/defensive-distillation
venue: S&P 2016
venue_date: 2016-05-23
dataset: MNIST
threat_model: $$\ell_0 (\epsilon = 112)$$
natural: 99.51% accuracy
claims: >
0.45% adversary success rate in changing classifier's prediction
analyses:
- claims: 3.6% accuracy
citation: CW16
name: Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
url: https://arxiv.org/abs/1511.04508
authors: Papernot et al.
code: https://github.com/lengstrom/defensive-distillation
venue: S&P 2016
venue_date: 2016-05-23
dataset: MNIST
threat_model: $$\ell_0 (\epsilon = 112)$$
natural: 99.51% accuracy
claims: >
0.45% adversary success rate in changing classifier's prediction
analyses:
- claims: 3.6% accuracy
citation: CW16
url: https://arxiv.org/abs/1608.04644
code: https://github.com/lengstrom/defensive-distillation
-
name: Deflecting Adversarial Attacks with Pixel Deflection
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