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Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Ehran, D., Goodfellow, I. J., and Fergus, R. (2013) · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y. (2014) · 2014
Earlier work this paper cites.
Analysis of classifiers robustness to adversarial perturbations
Fawzi, A., Fawzi, O., , and Frossard, P. (2015) · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2015) · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016) · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S. (2016) · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P. (2016) · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A. (2016b) · 2016
Cited alongside, same era.
Emergence of invariance and disentangling in deep representations
Achille, A. and Soatto, S. (2017) · 2017
Cited alongside, same era.
Fader networks: Manipulating images by sliding attributes
Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., and Ranzato, M. (2017) · 2017
On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B. (2017) · 2017
Later among the works it cites.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P. (2017) · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
Tramer, F., Kurakin, A., Papernot, N., Boneh, D., and McDaniel, P. (2017) · 2017
Later among the works it cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., N. Dauphin, Y., and Lopez-Paz, D. (2017) · 2017
Later among the works it cites.
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Cited alongside, same era.
Practical black-box attacks against deep learning systems using adversarial examples
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., and Swami, A. (2016a)
Cited in the paper.