Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |