ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks

H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Transactions on pattern analysis and machine intelligence , vol. 20, no. 1, pp. 23–38, 1998

1998

Earlier work this paper cites.

S. Abu-Nimeh, D. Nappa, X. Wang, and S. Nair, “A comparison of machine learning techniques for phishing detection,” in Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit . ACM, 2007, pp. 60–69

2007

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” International Conference on Learning Representations , 2014

2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” International Conference on Learning Representations , 2015

2015

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations , 2015

2015

Earlier work this paper cites.

I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning . MIT press Cambridge, 2016, vol. 1

2016

Earlier work this paper cites.

S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2574–2582

2016

Earlier work this paper cites.

N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in Security and Privacy (SP), 2016 IEEE Symposium on . IEEE, 2016, pp. 582–597

2016

Earlier work this paper cites.

W. Xu, Y. Qi, and D. Evans, “Automatically evading classifiers,” in Proceedings of the 2016 Network and Distributed Systems Symposium , 2016

2016

Earlier work this paper cites.

N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” pp. 39–57, 2017

2017

Earlier work this paper cites.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” International Conference on Learning Representations , 2017

2017

Cited alongside, same era.

G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer et al. , “Fader networks: Manipulating images by sliding attributes,” in Advances in Neural Information Processing Systems , 2017, pp. 5969–5978

2017

Cited alongside, same era.

B. Liang, H. Li, M. Su, X. Li, W. Shi, and X. Wang, “Detecting adversarial examples in deep networks with adaptive noise reduction,” arXiv preprint arXiv:1705.08378 , 2017

Original

2017

Cited alongside, same era.

G. Louppe, M. Kagan, and K. Cranmer, “Learning to pivot with adversarial networks,” in Advances in Neural Information Processing Systems , 2017, pp. 982–991

2017

Cited alongside, same era.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017

W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” arXiv preprint arXiv:1704.01155 , 2017

Original

2017

Later among the works it cites.

A. Athalye, N. Carlini, and D. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” arXiv preprint arXiv:1802.00420 , 2018

Original

2018

Later among the works it cites.

R. Benenson, “Classification datasets results,” 2018, [Online; accessed 06-April-2018]. [Online]. Available: http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

2018

Later among the works it cites.

H. Kannan, A. Kurakin, and I. Goodfellow, “Adversarial logit pairing,” arXiv preprint arXiv:1803.06373 , 2018

Original

2018

Later among the works it cites.