FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques

F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” arXiv preprint arXiv:1705.07204 , 2017

Original

2017

Cited alongside, same era.

D. Gong, J. Yang, L. Liu, Y. Zhang, I. Reid, C. Shen, A. Van Den Hengel, and Q. Shi, “From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2319–2328

2017

Cited alongside, same era.

K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1625–1634

2018

Cited alongside, same era.

X. Yuan, Y. Chen, Y. Zhao, Y. Long, X. Liu, K. Chen, S. Zhang, H. Huang, X. Wang, and C. A. Gunter, “Commandersong: A systematic approach for practical adversarial voice recognition,” in 27th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 18) , 2018, pp. 49–64

2018

Cited alongside, same era.

U. Shaham, Y. Yamada, and S. Negahban, “Understanding adversarial training: Increasing local stability of supervised models through robust optimization,” Neurocomputing , vol. 307, pp. 195–204, 2018

2018

Cited alongside, same era.

A. S. Ross and F. Doshi-Velez, “Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,” in Thirty-second AAAI conference on artificial intelligence , 2018

2018

Cited alongside, same era.

C. Guo, M. Rana, M. Cisse, and L. van der Maaten, “Countering adversarial images using input transformations,” in International Conference on Learning Representations , 2018

2018

Cited alongside, same era.

N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. Goodfellow, A. Madry, and A. Kurakin, “On evaluating adversarial robustness,” arXiv preprint arXiv:1902.06705 , 2019

Original

2019

Later among the works it cites.

X. Ling, S. Ji, J. Zou, J. Wang, C. Wu, B. Li, and T. Wang, “Deepsec: A uniform platform for security analysis of deep learning model,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 673–690

2019

Later among the works it cites.

E. Raff, J. Sylvester, S. Forsyth, and M. McLean, “Barrage of random transforms for adversarially robust defense,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6528–6537

2019

Later among the works it cites.

Z. Liu, Q. Liu, T. Liu, N. Xu, X. Lin, Y. Wang, and W. Wen, “Feature distillation: DNN-oriented jpeg compression against adversarial examples,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2019, pp. 860–868

2019

Later among the works it cites.

A. S. Rakin, Z. He, and D. Fan, “Bit-flip attack: Crushing neural network with progressive bit search,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1211–1220

2019

Later among the works it cites.

Y. Gao, C. Xu, D. Wang, S. Chen, D. C. Ranasinghe, and S. Nepal, “Strip: A defence against trojan attacks on deep neural networks,” in Proceedings of the 35th Annual Computer Security Applications Conference , 2019, pp. 113–125

2019

Later among the works it cites.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data , vol. 6, no. 1, p. 60, 2019

2019

Later among the works it cites.

D. Hendrycks and T. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,” arXiv preprint arXiv:1903.12261 , 2019

Original

2019

Later among the works it cites.

H. Qiu, Y. Zeng, Q. Zheng, T. Zhang, M. Qiu, and G. Memmi, “Mitigating advanced adversarial attacks with more advanced gradient obfuscation techniques,” arXiv preprint arXiv:2005.13712 , 2020

Original

2020

Closest in time.

M. Qiu and H. Qiu, “Review on image processing based adversarial example defenses in computer vision,” in 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) . IEEE, 2020, pp. 94–99

2020

Closest in time.

F. Tramer, N. Carlini, W. Brendel, and A. Madry, “On adaptive attacks to adversarial example defenses,” arXiv preprint arXiv:2002.08347 , 2020

Original

2020

Closest in time.