Understand
Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand.
- An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier.
- However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples.
- Most of the prior works have been focused on synthesizing adversarial samples in the image domain.
Built on
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 2001
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Learning word vectors for sentiment analysis
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
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Twitter gender classification dataset
CloudFlower · 2013
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Efficient Estimation of Word Representations in Vector Space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Similar
Mlaas: Machine learning as a service
M. Ribeiro, K. Grolinger, and M. A. M. Capretz · 2015
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Character-level convolutional networks for text classification
X. Zhang, J. J. Zhao, and Y. LeCun · 2015
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Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
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Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. D. McDaniel, and I. J. Goodfellow · 2016
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Deceiving Google’s Perspective API Built for Detecting Toxic Comments
H. Hosseini, S. Kannan, B. Zhang, and R. Poovendran · 2017
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Then
Adversarial attacks on neural network policies
S. H. Huang, N. Papernot, I. J. Goodfellow, Y. Duan, and P. Abbeel · 2017
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Adversarial examples for generative models
J. Kos, I. Fischer, and D. Song · 2017
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Project title
B. Kulynych · 2017
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Deep Text Classification Can be Fooled
B. Liang, H. Li, M. Su, P. Bian, X. Li, and W. Shi · 2017
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Practical black-box attacks against machine learning
N. Papernot, P. D. McDaniel, I. J. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
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