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Machine learning models have been shown to be vulnerable to adversarial examples.
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in Neural Information Processing Systems , 2017, pp. 5099–5108
2017
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2017
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H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3d object reconstruction from a single image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 605–613
2017
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A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in 5th International Conference on Learning Representations, ICLR 2017 , 2017
2017
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D. Su, H. Zhang, H. Chen, J. Yi, P.-Y. Chen, and Y. Gao, “Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 631–648
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A. Fawzi, H. Fawzi, and O. Fawzi, “Adversarial vulnerability for any classifier,” in Advances in Neural Information Processing Systems , 2018, pp. 1178–1187
2018
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L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, and A. Madry, “Adversarially robust generalization requires more data,” in Advances in Neural Information Processing Systems , 2018, pp. 5014–5026
2018
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A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
2019
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D. Liu, R. Yu, and H. Su, “Extending adversarial attacks and defenses to deep 3d point cloud classifiers,” in IEEE International Conference on Image Processing (ICIP) , 2019, pp. 2279–2283
2019
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2019
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2019
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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
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T. Zheng, C. Chen, J. Yuan, B. Li, and K. Ren, “Pointcloud saliency maps,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , Oct 2019, pp. 1598–1606
2019
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M. Wicker and M. Kwiatkowska, “Robustness of 3d deep learning in an adversarial setting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 767–11 775
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K. Wang, K. Chen, and K. Jia, “Deep cascade generation on point sets,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 3726–3732
2019
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J. Tang, X. Han, J. Pan, K. Jia, and X. Tong, “A skeleton-bridged deep learning approach for generating meshes of complex topologies from single rgb images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4541–4550
2019
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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
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R. Li, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng, “Pu-gan: a point cloud upsampling adversarial network,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 7203–7212
2019
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2020
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T. Tsai, K. Yang, T.-Y. Ho, and Y. Jin, “Robust adversarial objects against deep learning models,” in AAAI , 2020
2020
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