Efficient Joint Gradient Based Attack Against SOR Defense for 3D Point Cloud Classification
Chengcheng Ma, Weiliang Meng, Baoyuan Wu, Shibiao Xu, Xiaopeng Zhang
2020年份
49被引次数
14顶会引用
摘要
Deep learning based classifiers on 3D point cloud data have been shown vulnerable to adversarial examples, while a defense strategy named Statistical Outlier Removal (SOR) is widely adopted to defend adversarial examples successfully, by discarding outlier points in the point cloud.
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- PointBA: Towards Backdoor Attacks in 3D Point CloudXinke Li, Zhirui Chen, Yue Zhao, Zekun Tong 等ICCV 2021 · 被引用 62 次
- Generating Transferable 3D Adversarial Point Cloud via Random Perturbation FactorizationBangyan He, Jian Liu, Yiming Li, Siyuan Liang 等AAAI 2023 · 被引用 53 次
- Isometric 3D Adversarial Examples in the Physical WorldYibo Miao, Yinpeng Dong, Jun Zhu, Xiao-Shan GaoNeurIPS 2022 · 被引用 45 次
- Effective and Efficient Vote Attack on Capsule NetworksJindong Gu, Baoyuan Wu, Volker TrespICLR 2021 · 被引用 28 次
- Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit GradientsKaidong Li, Ziming Zhang, Cuncong Zhong, Guanghui WangCVPR 2022 · 被引用 24 次
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