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Although many efforts have been made into attack and defense on the 2D image domain in recent years, few methods explore the vulnerability of 3D models.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 1907–1915
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H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 945–953
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F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 815–823
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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 (CVPR) , 2016, pp. 2574–2582
2016
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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) , 2016, pp. 372–387
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J. Varley, C. DeChant, A. Richardson, J. Ruales, and P. Allen, “Shape completion enabled robotic grasping,” in IEEE international Conference on Intelligent Robots and Systems (IROS) , 2017, pp. 2442–2447
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 652–660
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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,” Advances in Neural Information Processing Systems (NIPS) , 2017
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M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” Advances in Neural Information Processing Systems (NIPS) , vol. 30, 2017
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S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 1765–1773
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N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in IEEE Symposium on Security and Privacy (SP) , 2017, pp. 39–57
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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 (CVPR) , 2017, pp. 605–613
2017
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D. Tian, H. Ochimizu, C. Feng, R. Cohen, and A. Vetro, “Geometric distortion metrics for point cloud compression,” in 2017 IEEE International Conference on Image Processing (ICIP) . IEEE, 2017, pp. 3460–3464
2017
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Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting adversarial attacks with momentum,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9185–9193
2018
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X. Yue, B. Wu, S. A. Seshia, K. Keutzer, and A. L. Sangiovanni-Vincentelli, “A lidar point cloud generator: from a virtual world to autonomous driving,” in Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval , 2018, pp. 458–464
2018
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Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 206–215
2018
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G. Te, W. Hu, A. Zheng, and Z. Guo, “Rgcnn: Regularized graph cnn for point cloud segmentation,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 746–754
2018
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 (CVPR) , 2019, pp. 11 767–11 775
2019
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T. Zheng, C. Chen, J. Yuan, B. Li, and K. Ren, “Pointcloud saliency maps,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 1598–1606
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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A. Mustafa, S. Khan, M. Hayat, R. Goecke, J. Shen, and L. Shao, “Adversarial defense by restricting the hidden space of deep neural networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 3385–3394
2019
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Cited alongside, same era.
B. Graham, M. Engelcke, and L. Van Der Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 9224–9232
2018
Cited alongside, same era.
T. Yu, J. Meng, and J. Yuan, “Multi-view harmonized bilinear network for 3d object recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 186–194
2018
Cited alongside, same era.
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” Advances in Neural Information Processing Systems (NIPS) , vol. 31, pp. 820–830, 2018
2018
Cited alongside, same era.
A. N. Bhagoji, W. He, B. Li, and D. Song, “Practical black-box attacks on deep neural networks using efficient query mechanisms,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 154–169
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 (ICLR) , 2018
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 (ICLR) , 2018
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 (CVPR) , 2018, pp. 1778–1787
2018
Cited alongside, same era.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in International Conference on Learning Representations (ICLR) , 2018
2018
Cited alongside, same era.
B. Zhong, H. Huang, and E. Lobaton, “Reliable vision-based grasping target recognition for upper limb prostheses,” IEEE Transactions on Cybernetics , 2020
2020
Later among the works it cites.
T. Tsai, K. Yang, T.-Y. Ho, and Y. Jin, “Robust adversarial objects against deep learning models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 954–962
2020
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Y. Wen, J. Lin, K. Chen, C. P. Chen, and K. Jia, “Geometry-aware generation of adversarial point clouds,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2020
2020
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Y. Rao, J. Lu, and J. Zhou, “Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 5376–5385
2020
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X. Gao, W. Hu, and G.-J. Qi, “Graphter: Unsupervised learning of graph transformation equivariant representations via auto-encoding node-wise transformations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 7163–7172
2020
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H. Lei, N. Akhtar, and A. Mian, “Spherical kernel for efficient graph convolution on 3d point clouds,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2020
2020
Later among the works it cites.
Y. Li, S. Bai, C. Xie, Z. Liao, X. Shen, and A. Yuille, “Regional homogeneity: Towards learning transferable universal adversarial perturbations against defenses,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2020, pp. 795–813
2020
Later among the works it cites.
J. Lin, C. Song, K. He, L. Wang, and J. E. Hopcroft, “Nesterov accelerated gradient and scale invariance for adversarial attacks,” in International Conference on Learning Representations (ICLR) , 2020
2020
Later among the works it cites.
Y. Zhao, Y. Wu, C. Chen, and A. Lim, “On isometry robustness of deep 3d point cloud models under adversarial attacks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 1201–1210
2020
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H. Zhou, D. Chen, J. Liao, K. Chen, X. Dong, K. Liu, W. Zhang, G. Hua, and N. Yu, “Lg-gan: Label guided adversarial network for flexible targeted attack of point cloud based deep networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 10 356–10 365
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Dong, D. Chen, H. Zhou, G. Hua, W. Zhang, and N. Yu, “Self-robust 3d point recognition via gather-vector guidance,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 11 513–11 521
2020
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2020
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
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2020
Later among the works it cites.
W. Hu, J. Pang, X. Liu, D. Tian, C.-W. Lin, and A. Vetro, “Graph Signal Processing for geometric data and beyond: Theory and applications,” accepted to IEEE Transactions on Multimedia , 2021
2021
Closest in time.
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, “Learning semantic segmentation of large-scale point clouds with random sampling,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2021
2021
Closest in time.
X. Zhu, H. Zhou, T. Wang, F. Hong, W. Li, Y. Ma, H. Li, R. Yang, and D. Lin, “Cylindrical and asymmetrical 3d convolution networks for lidar-based perception,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2021
2021
Closest in time.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 259–16 268
2021
Closest in time.
2021
Closest in time.
H. Liu, J. Jia, and N. Z. Gong, “Pointguard: Provably robust 3d point cloud classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6186–6195
2021
Closest in time.
X. Ma, C. Qin, H. You, H. Ran, and Y. Fu, “Rethinking network design and local geometry in point cloud: A simple residual mlp framework,” in International Conference on Learning Representations (ICLR) , 2022
2022
Closest in time.