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Adversarial examples are carefully perturbed in-puts for fooling machine learning models.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2016
Earlier work this paper cites.
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Miyato, T., Maeda, S.-i., Koyama, M., and Ishii, S · 2017
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Later among the works it cites.
Brendel, W., Rauber, J., Kurakin, A., Papernot, N., Veliqi, B., Salathé, M., Mohanty, S. P., and Bethge, M · 2018
Later among the works it cites.
Unrestricted adversarial examples
Brown, T. B., Carlini, N., Zhang, C., Olsson, C., Christiano, P., and Goodfellow, I · 2018
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Improving the generalization of adversarial training with domain adaptation
Song, C., He, K., Wang, L., and Hopcroft, J. E · 2018
Later among the works it cites.
Theoretically principled trade-off between robustness and accuracy
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Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J
Cited in the paper.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J
Cited in the paper.
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2019
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