Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training

Carlini, N., Wagner, D.A.: Defensive distillation is not robust to adversarial examples. CoRR abs/1607.04311

Original

2016

Earlier work this paper cites.

Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In: ICML. pp. 274–283. PMLR (2018)

2018

Earlier work this paper cites.

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018), https://openreview.net/forum?id=rJzIBfZAb

2018

Earlier work this paper cites.

Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J.C., Liang, P.S.: Unlabeled data improves adversarial robustness. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019), https://proceedings.neurips.cc/paper/2019/file/32e0bd1497aa43e02a42f47d9d6515ad-Paper.pdf

2019

Cited alongside, same era.

Wang, Y., Ma, X., Bailey, J., Yi, J., Zhou, B., Gu, Q.: On the convergence and robustness of adversarial training. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6586–6595. PMLR (09–15 Jun 2019), http://proceedings.mlr.press/v97/wang19i.html

2019

Cited alongside, same era.

Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: ICML. pp. 2206–2216. PMLR (2020)

2020

Cited alongside, same era.

Zhang, J., Xu, X., Han, B., Niu, G., Cui, L., Sugiyama, M., Kankanhalli, M.: Attacks which do not kill training make adversarial learning stronger. In: III, H.D., Singh, A. (eds.) ICML. Proceedings of Machine Learning Research, vol. 119, pp. 11278–11287. PMLR (13–18 Jul 2020), http://proceedings.mlr.press/v119/zhang20z.html

2020

Later among the works it cites.

Zhang, J., Zhu, J., Niu, G., Han, B., Sugiyama, M., Kankanhalli, M.: Geometry-aware instance-reweighted adversarial training. In: ICLR (2021)

2021

Closest in time.