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Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output.
Random sampling with a reservoir
J. S. Vitter · 1985
Earlier work this paper cites.
The mnist database of handwritten digits, 1998
Y. LeCun and C. Cortes · 1998
Earlier work this paper cites.
Can machine learning be secure?
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar · 2006
Earlier work this paper cites.
Pattern recognition
C. M. Bishop · 2006
Earlier work this paper cites.
Model compression
C. Bucila, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Theano: a cpu and gpu math expression compiler
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, and al · 2010
Earlier work this paper cites.
Online anomaly detection under adversarial impact
M. Kloft and P. Laskov · 2010
Earlier work this paper cites.
Support vector machines under adversarial label noise
B. Biggio, B. Nelson, and P. Laskov · 2011
Earlier work this paper cites.
Adversarial machine learning
L. Huang, A. D. Joseph, B. Nelson, B. I. Rubinstein, and J. Tygar · 2011
Cited alongside, same era.
Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and L. Pavel · 2012
Cited alongside, same era.
Machine learning: a probabilistic perspective
K. P. Murphy · 2012
Cited alongside, same era.
Evasion attacks against machine learning at test time
B. Biggio, I. Corona, and al · 2013
Cited alongside, same era.
Security evaluation of pattern classifiers under attack
B. Biggio, G. Fumera, and F. Roli · 2014
Cited alongside, same era.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Later among the works it cites.
Net2net: Accelerating learning via knowledge transfer
T. Chen, I. Goodfellow, and J. Shlens · 2016
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Machine Learning in Adversarial Settings
P. McDaniel, N. Papernot, and Z. B. Celik · 2016
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The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, and al · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, and al · 2016
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, , et al · 2014
Cited alongside, same era.
Lasagne: Lightweight library to build and train neural networks in theano, 2015
E. Battenberg, S. Dieleman, and al · 2015
Cited alongside, same era.
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
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