[33] Naveed Akhtar and Ajmal Mian. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6:14410-14430, 2018.
[34] Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, and Cho-Jui Hsieh. On the robustness of self-attentive models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1520–1529, 2019.
[35] Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, and Cho-Jui Hsieh. Robustness verification for transformers. In International Conference on Learning Representations, 2020.
[36] Gregory Bonaert, Dimitar I Dimitrov, Maximilian Baader, and Martin Vechev. Fast and precise certification of transformers. In Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, pages 466-481, 2021.
[37] Kaleel Mahmood, Rigel Mahmood, and Marten Van Dijk. On the robustness of vision transformers to adversarial examples. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7838-7847, 2021.
[38] Hang Zhou, Dongdong Chen, Jing Liao, Kejiang Chen, Xiaoyi Dong, Kunlin Liu, Weiming Zhang, Gang Hua, and Nenghai Yu. LG-GAN: Label guided adversarial network for flexible targeted attack of point cloud based deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10356-10365, 2020.
[39] Yuxin Wen, Jiehong Lin, Ke Chen, CL Philip Chen, and Kui Jia. Geometry-aware generation of adversarial point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
[40] Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, and Nenghai Yu. Shape-invariant 3d adversarial point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15335-15344, June 2022.
[41] Daniel Liu, Ronald Yu, and Hao Su. Adversarial shape perturbations on 3d point clouds. In European Conference on Computer Vision, pages 88-104. Springer, 2020.
[42] Tobias Lorenz, Anian Ruoss, Mislav Balunović, Gagandeep Singh, and Martin Vechev. Robustness certification for point cloud models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7608-7618, 2021.
[43] Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong. PointGuard: Provably robust 3d point cloud classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6186-6195, 2021.
[44] Dishanika Dewani Denipitiyage, Thalaiyasingam Ajanthan, Parameswaran Kamalaruban, and Adrian Weller. Provable defense against clustering attacks on 3d point clouds. In The AAAI-22 Workshop on Adversarial Machine Learning and Beyond, 2021.
[45] Jaeyeon Kim, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung. Minimal adversarial examples for deep learning on 3d point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7797–7806, 2021.
[46] Wenda Chu, Linyi Li, and Bo Li. TPC: Transformation-specific smoothing for point cloud models. In International Conference on Machine Learning, 2022.
[47] Simon Geisler, Daniel Zügner, and Stephan Gunnemann. Reliable graph neural networks via robust aggregation. In Advances in Neural Information Processing Systems, 2020.
[48] Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Gunnemann. Robustness of graph neural networks at scale. In Advances in Neural Information Processing Systems, 2021.
[49] Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, and Stephan Gunnemann. Randomized message-interception smoothing: Gray-box certificates for graph neural networks. In Advances in Neural Information Processing Systems, 2022.