Regularizers for Single-step Adversarial Training

A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” Tech. Rep., 2009

2009

Earlier work this paper cites.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in International Conference on Learning Representations (ICLR) , 2013

2013

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” in International Conference on Learning Representations (ICLR) , 2015

2015

Earlier work this paper cites.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015

2015

Earlier work this paper cites.

G. K. Dziugaite, Z. Ghahramani, and D. M. Roy, “A study of the effect of jpg compression on adversarial images,” arXiv preprint arXiv:1608.00853 , 2016

Original

2016

Earlier work this paper cites.

S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks,” in The IEEE Computer Vision and Pattern Recognition (CVPR) , 2016

2016

Earlier work this paper cites.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 , 2016

Original

2016

Earlier work this paper cites.

N. Papernot, P. D. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks,” in IEEE Symposium on Security and Privacy, SP 2016, San Jose, CA, USA, May 22-26, 2016 , 2016, pp. 582–597

2016

Earlier work this paper cites.

S. Zagoruyko and N. Komodakis, “Wide residual networks,” in Proceedings of the British Machine Vision Conference (BMVC) , E. R. H. Richard C. Wilson and W. A. P. Smith, Eds. BMVA Press, September 2016, pp. 87.1–87.12. [Online]. Available: https://dx.doi.org/10.5244/C.30.87

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778

2016

Earlier work this paper cites.

N. Papernot, P. D. McDaniel, I. J. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples,” in Asia Conference on Computer and Communications Security (ASIACCS) , 2017

2017

Earlier work this paper cites.

Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into Transferable Adversarial Examples and Black-box Attacks,” in International Conference on Learning Representations (ICLR) , 2017

2017

Earlier work this paper cites.

A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial Machine Learning at Scale,” in International Conference on Learning Representations (ICLR) , 2017

2017

Cited alongside, same era.

R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner, “Detecting adversarial samples from artifacts,” arXiv preprint arXiv:1703.00410 , 2017

Original

2017

Cited alongside, same era.

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

Original

2017

Cited alongside, same era.

N. Carlini and D. A. Wagner, “Towards Evaluating the Robustness of Neural Networks,” in 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22-26, 2017 , 2017, pp. 39–57

2017

Cited alongside, same era.

G. S. Dhillon, K. Azizzadenesheli, J. D. Bernstein, J. Kossaifi, A. Khanna, Z. C. Lipton, and A. Anandkumar, “Stochastic Activation Pruning for Robust Adversarial Defense,” in International Conference on Learning Representations (ICLR) , 2018

2018

Later among the works it cites.

C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille, “Mitigating Adversarial Effects Through Randomization,” in International Conference on Learning Representations (ICLR) , 2018

2018

Later among the works it cites.

Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman, “PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples,” in International Conference on Learning Representations (ICLR) , 2018

2018

Later among the works it cites.

P. Samangouei, M. Kabkab, and R. Chellappa, “Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models,” in International Conference on Learning Representations (ICLR) , 2018

2018

Later among the works it cites.