Deep Manifold Attack on Point Clouds via Parameter Plane Stretching
Authors: Keke Tang, Jianpeng Wu, Weilong Peng, Yawen Shi, Peng Song, Zhaoquan Gu, Zhihong Tian, Wenping Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones. |
| Researcher Affiliation | Academia | 1 Guangzhou University 2 Singapore University of Technology and Design 3 Harbin Institute of Technology (Shenzhen) 4 Peng Cheng Laboratory 5 Texas A&M University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We adopt two public datasets for evaluation: Shape Net Part (Chang et al. 2015) and Model Net40 (Wu et al. 2015). |
| Dataset Splits | Yes | We select 14007 point clouds for training and 2874 for testing on Shape Net Part, while 9843 for training and 2468 for testing on Model Net40 following (Qi et al. 2017b). |
| Hardware Specification | Yes | Both the pretrain of manifold auto-encoder and the training of manifold attack framework are performed on a workstation with one NVIDIA RTX 2080Ti GPU for 1000 epochs. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not specify a version number for it or any other key software dependencies. |
| Experiment Setup | Yes | the mapping representation θP is denoted with the codeword in size of 1 1024, and the parameter plane is denoted with a 45 45 point grid in the range of [ 0.3, 0.3]. For TPS transformation, we use 4 4 control points. The offset prediction network Fo is implemented with MLP (3 64 128 1024)-Max Pool-FC (1024 512 256 32)-Tanh to predict the offsets of control points along two axes in the range of [ 1.0, 1.0]. [...] α is a weighting parameter, setting as 0.2 in our paper. |