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Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs).
Y. Zhao, H. Zhu, R. Liang, Q. Shen, S. Zhang, and K. Chen, “Seeing isn’t believing: Towards more robust adversarial attack against real world object detectors,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security , 2019, pp. 1989–2004
2004
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
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A. Hore and D. Ziou, “Image quality metrics: Psnr vs. ssim,” in 2010 20th International Conference on Pattern Recognition . IEEE, 2010, pp. 2366–2369
2010
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2013
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
2014
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2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
N. Carlini, P. Mishra, T. Vaidya, Y. Zhang, M. Sherr, C. Shields, D. Wagner, and W. Zhou, “Hidden voice commands,” in 25th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 16) , 2016, pp. 513–530
2016
Earlier work this paper cites.
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, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
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 2016 IEEE Symposium on Security and Privacy (SP) . IEEE, 2016, pp. 582–597
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
G. Zhang, C. Yan, X. Ji, T. Zhang, T. Zhang, and W. Xu, “Dolphinattack: Inaudible voice commands,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 103–117
2017
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 39–57
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
D. Meng and H. Chen, “Magnet: a two-pronged defense against adversarial examples,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 135–147
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
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
Later among the works it cites.
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 , 2018, pp. 196–204
2018
Later among the works it cites.
W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” in 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21, 2018 . The Internet Society, 2018. [Online]. Available: http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018_03A-4_Xu_paper.pdf
2018
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
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.
C. Guo, M. Rana, M. Cisse, and L. van der Maaten, “Countering adversarial images using input transformations,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=SyJ7ClWCb
2018
Cited alongside, same era.
A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. Storer, “Deflecting adversarial attacks with pixel deflection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8571–8580
2018
Cited alongside, same era.
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille, “Mitigating adversarial effects through randomization,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=Sk9yuql0Z
2018
Cited alongside, same era.
F. Liao, M. Liang, Y. Dong, T. Pang, X. Hu, and J. Zhu, “Defense against adversarial attacks using high-level representation guided denoiser,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1778–1787
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Cao, C. Xiao, B. Cyr, Y. Zhou, W. Park, S. Rampazzi, Q. A. Chen, K. Fu, and Z. M. Mao, “Adversarial sensor attack on lidar-based perception in autonomous driving,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security , 2019, pp. 2267–2281
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.
2019
Later among the works it cites.
J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Transactions on Evolutionary Computation , vol. 23, no. 5, pp. 828–841, 2019
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.
D. Yin, R. G. Lopes, J. Shlens, E. D. Cubuk, and J. Gilmer, “A fourier perspective on model robustness in computer vision,” in Advances in Neural Information Processing Systems , 2019, pp. 13 255–13 265
2019
Later among the works it cites.
Y. Yang, G. Zhang, D. Katabi, and Z. Xu, “Me-net: Towards effective adversarial robustness with matrix estimation,” in International Conference on Machine Learning , 2019, pp. 7025–7034
2019
Later among the works it cites.
“A complete list of all (arxiv) adversarial example papers,” https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html , 2020
2020
Closest in time.