Keyword [Universal Adversarial Perturbations]

Akhtar N, Liu J, Mian A. Defense against universal adversarial perturbations[C]Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3389-3398.

Keyword [DQN]

Bellver M, Giroinieto X, Marques F, et al. Hierarchical Object Detection with Deep Reinforcement Learning[J]. Advances in Parallel Computing, 2016

Keyword [Ensemble]

Xie C, Wang J, Zhang Z, et al. Mitigating adversarial effects through randomization[J]. arXiv preprint arXiv:1711.01991, 2017.

Prakash A, Moran N, Garber S, et al. Deflecting adversarial attacks with pixel deflection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8571-8580.

Baluja S, Fischer I. Adversarial transformation networks: Learning to generate adversarial examples[J]. arXiv preprint arXiv:1703.09387, 2017.

Keyword [HGD]

Liao F, Liang M, Dong Y, et al. Defense against adversarial attacks using high-level representation guided denoiser[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 1778-1787.

Guo C, Rana M, Cisse M, et al. Countering adversarial images using input transformations[J]. arXiv preprint arXiv:1711.00117, 2017.

Keyword [MI-FGSM] [Ensemble]

Dong Y, Liao F, Pang T, et al. Boosting adversarial attacks with momentum[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 9185-9193.

Xie C, Wang J, Zhang Z, et al. Adversarial examples for semantic segmentation and object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1369-1378.

Arnab A, Miksik O, Torr P H S. On the robustness of semantic segmentation models to adversarial attacks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 888-897.