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..
Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors
Authors: Chen Ma, Xinjie Xu, Shuyu Cheng, Qi Xuan
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the Image Net and CIFAR-10 datasets show that our approach significantly outperforms 11 state-of-the-art methods in terms of query efficiency. |
| Researcher Affiliation | Collaboration | 1 Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China 2 Binjiang Institute of Artificial Intelligence, ZJUT, Hangzhou 310056, China 3 JQ Investments, Shanghai 200122, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Prior-Sign-OPT and Prior-OPT attack |
| Open Source Code | Yes | To further support reproducibility, we provide the complete attack code for our approach and all baseline methods at https://github.com/machanic/hard_label_attacks. |
| Open Datasets | Yes | All experiments are conducted on two datasets, i.e., CIFAR-10 (Krizhevsky & Hinton, 2009) and Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | We randomly select 1,000 images from the validation sets for experiments. |
| Hardware Specification | Yes | The experiments of all methods are conducted using Py Torch 1.7.1 framework on NVIDIA V100 and A100 GPUs. |
| Software Dependencies | Yes | The experiments of all methods are conducted using Py Torch 1.7.1 framework on NVIDIA V100 and A100 GPUs. |
| Experiment Setup | Yes | Table 3: The hyperparameters of Prior-OPT and Prior-Sign-OPT. Dataset Hyperparameter Value q, total number of vectors for estimating a gradient, including priors and random vectors 200 the binary search s stopping threshold β 500 the number of iterations 1,000 gmax, the maximum gradient norm for the gradient clipping operation 0.1 |