Rapid Plug-in Defenders
Authors: Kai Wu, yujian li, Jian Lou, Xiaoyu Zhang, Handing Wang, Jing Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation centers on assessing Ce Ta D s effectiveness, transferability, and the impact of different components in scenarios involving one-shot adversarial examples. |
| Researcher Affiliation | Academia | Kai Wu Xidian University kwu@xidian.edu.cn Yujian Betterest Li Xidian University bebetterest@outlook.com Jian Lou Zhejiang University jian.lou@hoiying.net Xiaoyu Zhang Xidian University xiaoyuzhang@xidian.edu.cn Handing Wang Xidian University hdwang@xidian.edu.cn Jing Liu Xidian University neouma@mail.xidian.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: See Section 4 and Appendix Section A. Moreover, Code of Ce Ta D is easy to implement. |
| Open Datasets | Yes | Three image classification datasets, MNIST [21], CIFAR-10 [20], CIFAR-100 [20], and Imagenet-1k[34], are utilized. |
| Dataset Splits | No | For simplicity, the training set only consists of adversarial examples whose number equals to that of the classes, namely one-shot. The paper does not explicitly mention a distinct validation set split. |
| Hardware Specification | Yes | CPU, 14 v CPU Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz; GPU, 1 NVIDIA RTX 3090(24GB). |
| Software Dependencies | No | Optimization loops are implemented by Py Torch. The implementation of Lion ([6]), the optimizer which we apply, is available at https: //github.com/lucidrains/lion-pytorch. (No specific version numbers for PyTorch or Lion are provided). |
| Experiment Setup | Yes | In our default setup, only layer norm parameters (48 parameter groups, 36864 variables in total) are fine-tuned using Lion [6] with default hyper-parameters. We optimize Eq. (2) over 500 epochs with a batch size of 32. |