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Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints.
An elementary proof of a theorem of johnson and lindenstrauss
Dasgupta, S. and Gupta, A. (2003) · 2003
Earlier work this paper cites.
A robust minimax approach to classification
Lanckriet, G., Ghaoui, L., Bhattacharyya, C., and Jordan, M. (2003) · 2003
Earlier work this paper cites.
Manifolds and differential geometry
Lee, J. M. (2009) · 2009
Earlier work this paper cites.
Robustness and regularization of support vector machines
Xu, H., Caramanis, C., and Mannor, S. (2009) · 2009
Earlier work this paper cites.
Robust optimization in machine learning
Caramanis, C., Mannor, S., and Xu, H. (2012) · 2012
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012) · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Towards deep neural network architectures robust to adversarial examples
Gu, S. and Rigazio, L. (2014) · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Cited alongside, same era.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
Cited alongside, same era.
Analysis of classifiers’ robustness to adversarial perturbations
Fawzi, A., Fawzi, O., and Frossard, P. (2015) · 2015
Cited alongside, same era.
Learning with a strong adversary
Huang, R., Xu, B., Schuurmans, D., and Szepesvári, C. (2015) · 2015
Later among the works it cites.
Foveation-based mechanisms alleviate adversarial examples
Luo, Y., Boix, X., Roig, G., Poggio, T., and Zhao, Q. (2015) · 2015
Later among the works it cites.
Shaham, U., Yamada, Y., and Negahban, S. (2015) · 2015
Later among the works it cites.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P. (2016) · 2016
Closest in time.
Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J. (2016) · 2016
Closest in time.
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Manitest: Are classifiers really invariant?
Fawzi, A. and Frossard, P. (2015) · 2015
Cited alongside, same era.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Exploring the space of adversarial images
Tabacof, P. and Valle, E. (2016) · 2016
Closest in time.
Suppressing the unusual: towards robust cnns using symmetric activation functions
Zhao, Q. and Griffin, L. D. (2016) · 2016
Closest in time.