The Efficacy of SHIELD under Different Threat Models

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE Conference on Computer Vision and Pattern Recognition

2009

Earlier work this paper cites.

https://openreview.net/forum?id=kklr_MTHMRQjG

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in Proceedings of the 2nd International Conference on Learning Representations (ICLR) · 2014

Earlier work this paper cites.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572

Original

2014

Earlier work this paper cites.

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

Original

2016

Earlier work this paper cites.

http://doi.acm.org/10.1145/3128572.3140444

N. Carlini and D. Wagner, “Adversarial examples are not easily detected: Bypassing ten detection methods,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (AISec) · 2017

Earlier work this paper cites.

https://arxiv.org/abs/1712.03141

Original

B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” CoRR · 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

2017

Cited alongside, same era.

https://machine-learning-and-security.github.io/papers/mlsec17_paper_54.pdf

R. Shin and D. Song, “Jpeg-resistant adversarial images,” in Proceedings of the Machine Learning and Computer Security Workshop · 2017

Cited alongside, same era.

https://www.usenix.org/conference/woot17/workshop-program/presentation/he

W. He, J. Wei, X. Chen, N. Carlini, and D. Song, “Adversarial example defense: Ensembles of weak defenses are not strong,” in Proceedings of the 11th USENIX Workshop on Offensive Technologies (WOOT) · 2017

Cited alongside, same era.

https://machine-learning-and-security.github.io/papers/mlsec17_paper_27.pdf

Original

T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer, “Adversarial patch,” in Proceedings of the Machine Learning and Computer Security Workshop · 2017

Cited alongside, same era.

http://doi.acm.org/10.1145/3219819.3219910

N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau, “Shield: Fast, practical defense and vaccination for deep learning using jpeg compression,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · 2018

https://openreview.net/forum?id=rJzIBfZAb

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in Proceedings of the 6th International Conference on Learning Representations (ICLR) · 2018

Later among the works it cites.

J. Gilmer, R. P. Adams, I. Goodfellow, D. Andersen, and G. E. Dahl, “Motivating the rules of the game for adversarial example research,” arXiv preprint arXiv:1807.06732

Original

2018

Later among the works it cites.

http://arxiv.org/abs/1610.00768

Original

N. Papernot, F. Faghri, N. Carlini, I. Goodfellow, R. Feinman, A. Kurakin, C. Xie, Y. Sharma, T. Brown, A. Roy, A. Matyasko, V. Behzadan, K. Hambardzumyan, Z. Zhang, Y.-L. Juang, Z. Li, R. Sheatsley, A. Garg, J. Uesato, W. Gierke, Y. Dong, D. Berthelot, P. Hendricks, J. Rauber, and R. Long, “Technical report on the cleverhans v2.1.0 adversarial examples library,” arXiv preprint arXiv:1610.00768 · 2018

Later among the works it cites.

https://doi.org/10.1109/CVPR.2018.00957

Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2018

Later among the works it cites.