Fetching the paper…
Reading the bibliography…
It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g.
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks 32
2012
Earlier work this paper cites.
Montufar, G., Pascanu, R., Cho, K., Bengio, Y.: On the number of linear regions of deep neural networks. In: NIPS (2014)
2014
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. In: ICLR. pp. 2503–2511 (2014)
2014
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)
2015
Earlier work this paper cites.
Gurobi Optimization, I.: Gurobi optimizer reference manual (2016), http://www.gurobi.com
2016
Earlier work this paper cites.
Huang, R., Xu, B., Schuurmans, D., Szepesvari, C.: Learning with a strong adversary. In: ICLR (2016)
2016
Earlier work this paper cites.
Papernot, N., McDonald, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep networks. In: IEEE Symposium on Security & Privacy (2016)
2016
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, P.F.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR. pp. 2574–2582 (2016)
2016
Earlier work this paper cites.
2017
Cited alongside, same era.
Carlini, N., Wagner, D.: Adversarial examples are not easily detected: Bypassing ten detection methods. In: ACM Workshop on Artificial Intelligence and Security (2017)
2017
Cited alongside, same era.
Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy. pp. 39–57 (2017)
2017
Cited alongside, same era.
Hein, M., Andriushchenko, M.: Formal guarantees on the robustness of a classifier against adversarial manipulation. In: NIPS (2017)
2017
Cited alongside, same era.
Katz, G., Barrett, C., Dill, D., Julian, K., Kochenderfer, M.: Reluplex: An efficient smt solver for verifying deep neural networks. In: CAV (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Arora, R., Basuy, A., Mianjyz, P., Mukherjee, A.: Understanding deep neural networks with rectified linear unit. In: ICLR (2018)
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: ICLR Workshop (2017)
2017
Cited alongside, same era.
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: ICLR (2017)
2017
Cited alongside, same era.
Moosavi-Dezfooli, S., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)
2017
Cited alongside, same era.
Krizhevsky, A., Nair, V., Hinton, G.: Cifar-10 (canadian institute for advanced research) http://www.cs.toronto.edu/˜kriz/cifar.html
Cited in the paper.
Rauber, J., Brendel, W., Bethge, M.: Foolbox: A python toolbox to benchmark the robustness of machine learning models
Cited in the paper.
2018
Closest in time.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Valdu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)
2018
Closest in time.
Raghunathan, A., Steinhardt, J., Liang, P.: Certified defenses against adversarial examples. In: ICLR (2018)
2018
Closest in time.