ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Authors: Yinpeng Dong, Shouwei Ruan, Hang Su, Caixin Kang, Xingxing Wei, Jun Zhu
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 viewpoint robustness of image classifiers on the Image Net [45] dataset. Our results demonstrate that View Fool can effectively generate a distribution of adversarial viewpoints against the common image classifiers, which also exhibit high transferability across different models. |
| Researcher Affiliation | Collaboration | 1 Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center, Tsinghua University, Beijing 100084, China 2 Institute of Artificial Intelligence, Beihang University, Beijing 100191, China 3 Real AI 4 Peng Cheng Laboratory 5 Pazhou Laboratory (Huangpu), Guangzhou, China |
| Pseudocode | No | The paper describes its optimization algorithm in Section 3.3 using mathematical equations and textual explanations, but it does not present it as a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code to reproduce the experimental results is publicly available at https://github.com/Heathcliff-saku/View Fool_. |
| Open Datasets | Yes | We consider visual recognition models on Image Net [45] in the experiments. ... Moreover, we introduce a new OOD dataset called Image Net-V to benchmark viewpoint robustness. |
| Dataset Splits | No | The paper mentions evaluating models on ImageNet and ImageNet-V, but it does not explicitly specify the proportions or sizes of training, validation, or test splits used for its experiments. |
| Hardware Specification | No | The provided paper text does not contain specific details about the hardware used for experiments, such as exact GPU/CPU models or memory specifications. While the ethics checklist points to Appendix C.1 for this information, Appendix C.1 itself is not included in the provided text. |
| Software Dependencies | No | The paper mentions tools like "COLMAP [48]" and the "Adam optimizer [28]" but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In View Fool, we initialize the camera at [0, 4, 0] as shown in Fig. 2. We set the range of rotation angles as 2 [ 180 , 180 ], 2 [ 30 , 30 ], φ 2 [20 , 160 ], and the range of translation distances as x 2 [ 0.5, 0.5], y 2 [ 1, 1], z 2 [ 0.5, 0.5]. ... We set λ = 0.01 in the experiments ... We approximate the gradients in Eq. (6) with k = 50 MC samples and adopt the Adam optimizer [28] to update the distribution parameters (µ, σ) for 100 iterations. |