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..
Attacking Transformers with Feature Diversity Adversarial Perturbation
Authors: Chenxing Gao, Hang Zhou, Junqing Yu, YuTeng Ye, Jiale Cai, Junle Wang, Wei Yang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to test our method on Vi T-based models, CNN models, and MLP models. Furthermore, we assess the cross-task transferability of our attack method. |
| Researcher Affiliation | Collaboration | Chenxing Gao1, Hang Zhou1, Junqing Yu1, Yu Teng Ye1, Jiale Cai1, Wei Yang1* 1Huazhong University of Science and Technology, Wuhan, China Junle Wang2 2Tencent |
| Pseudocode | Yes | Algorithm 1: Feature Diversity Adversarial Perturbation on Vi Ts |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | Dataset: Similar to the settings in Dong(Dong et al. 2018), we randomly select data 1000 images from the validation set Image Net 2012 (Russakovsky et al. 2015). |
| Dataset Splits | No | The paper mentions using 1000 images from the validation set of ImageNet 2012, but it does not provide specific train/validation/test dataset splits for its own experimental setup. |
| Hardware Specification | No | The computation is completed in the HPC Platform of Huazhong University of Science and Technology. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for its dependencies. |
| Experiment Setup | Yes | Attack settings: we conduct attacks using a maximum perturbation value of ϵ = 16, the total number of attack iterations is N = 30, and the step size α = 3/255. |