Isometric 3D Adversarial Examples in the Physical World
Authors: yibo miao, Yinpeng Dong, Jun Zhu, Xiao-Shan Gao
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the performance of our method on attacking typical point cloud recognition models [48, 49, 68]. Results demonstrate that, in comparison with the alternative state-of-the-art attack methods [63, 69, 88], ϵ-ISO attack achieves higher success rates, while making the generated adversarial examples more natural and robust under physical transformations. A physical-world experiment is conducted by 3D-printing the adversarial meshes and re-scanning the objects for evaluation, which also validates the effectiveness of our method. |
| Researcher Affiliation | Collaboration | Yibo Miao1,3 , Yinpeng Dong2,3 , Jun Zhu2,3,4,5, Xiao-Shan Gao1 1 KLMM, UCAS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China 2 Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center, Tsinghua University, Beijing 100084, China 3 Real AI 4 Peng Cheng Laboratory 5 Pazhou Laboratory (Huangpu), Guangzhou, China |
| Pseudocode | Yes | As shown in the overall algorithm (in Appendix B), we update the Bayesian posterior distribution of the objective f using the observations obtained from the previous iterations in the gradient descent process. |
| Open Source Code | Yes | The code is included in the supplementary material. |
| Open Datasets | Yes | We use the Model Net40 [71] dataset in our experiments. This dataset contains 12,311 CAD models with 40 common object semantic categories in the real world. We use the official split [48, 49], where 9,843 examples are used for training and the remaining 2,468 examples are used for testing. |
| Dataset Splits | No | The paper specifies training and testing splits, but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper mentions physical hardware used for scanning and printing: 'The selected meshes are printed by the Stratsys J850 Prime 3D printer and scanned by the Ein Scan-SE 3D scanner.' It also mentions 'High Performance Computing Center, Tsinghua University' in the acknowledgements. However, it does not provide specific details about the computational hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam [31] to optimize the objective' but does not specify version numbers for any software libraries, programming languages, or other dependencies. |
| Experiment Setup | Yes | We use a fixed learning schedule of 250 iterations, where the learning rate and momentum are respectively set as 0.01 and 0.9. We assign the weighting parameters λ1 = 1.0, λ2 = 0.2 and λ3 = 0.8. The balancing parameter β is initialized as 1,500 and automatically adjusted by conducting 10 runs of binary search following [4]. |