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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
GSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack
Authors: Md Farhamdur Reza, Richeng Jin, Tianfu Wu, Huaiyu Dai
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 39,560 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,303 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on Image Net and PASCAL VOC datasets validate the effectiveness of GSBAK in crafting top-K adversarial examples. |
| Researcher Affiliation | Academia | Md Farhamdur Reza1 , Richeng Jin2 , Tianfu Wu1 & Huaiyu Dai1 1NC State University 2Zhejiang University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: GSBAK |
| Open Source Code | Yes | The code of our attack is available at https://github.com/Farhamdur/GSBA-K. |
| Open Datasets | Yes | Extensive experimental results on Image Net and PASCAL VOC datasets validate the effectiveness of GSBAK in crafting top-K adversarial examples. |
| Dataset Splits | Yes | In the case of untargeted attacks against a classifier on Image Net, we randomly select 1000 images that are correctly classified by the respective classifier. For targeted attacks, we create 1000 sets of images, each comprising a benign image xs and a target image xt. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or processor types. |
| Software Dependencies | No | The pre-trained Res Net-50, Res Net-101 and VGG-16 models are sourced from Py Torch. |
| Experiment Setup | Yes | In the case of GSBAK, we use reduced-dimensional frequency subspace with a dimension reduction factor f = 4 to sample low-frequency noise {zi}. We set the base query number I0 = 30, step size ϵ = 6, and tolerance τ = 0.0001. |