GenSeg: On Generating Unified Adversary for Segmentation
Authors: Yuxuan Zhang, Zhenbo Shi, Wei Yang, Shuchang Wang, Shaowei Wang, Yinxing Xue
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of Gen Seg in black-box attacks compared with state-of-the-art attacks. To evaluate the effectiveness of Gen Seg, we conduct comprehensive experiments on SS, IS, and PS, respectively. We employ 9 datasets and 15 models in total to validate our method. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Technology, University of Science and Technology of China 2 Suzhou Institute for Advanced Research, University of Science and Technology of China 3 Hefei National Laboratory, University of Science and Technology of China 4 Institute of Artificial Intelligence and Blockchain, Guangzhou University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Procedures are described using mathematical formulas and descriptive text. |
| Open Source Code | Yes | Code is available at: https://github.com/YXZhang979/Gen Seg |
| Open Datasets | Yes | To evaluate the attack effectiveness of Gen Seg, we employ the commonly used Pascal VOC (20 classes) [Everingham et al., 2010], Cityscapes (19 classes) [Cordts et al., 2016], and ADE20K (150 classes) [Zhou et al., 2017] for SS. For IS, we use the widely used City Scapes (8 things), COCO (80 things) [Lin et al., 2014], and ADE20K (100 things). As to PS, we adopt COCO (80 things and 53 stuff), Cityscapes (8 things and 11 stuff), and ADE20K (100 things and 50 stuff). |
| Dataset Splits | No | The paper mentions using well-known datasets and training models, but it does not explicitly provide specific percentages, sample counts, or predefined citations for training/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using specific models and optimizers (e.g., Adam optimizer, ResNet-based model) but does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'CUDA 11.1'). |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 5e-3 (β1 = .5, β2 = .999) for 100 epochs. We set the perturbation budget ϵ to the typical value of 8/255. Besides, we set attack iteration to 5, striking a balance between attack capability and efficiency. |