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..
Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training
Authors: Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, DAVID DOERMANN, Mingchen Gao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on defending against both decision-based and scorebased DFME demonstrate that Me Co can significantly reduce the effectiveness of existing DFME methods and substantially improve running efficiency. |
| Researcher Affiliation | Collaboration | 1University of Maryland, College Park, USA 2JD Explore Academy, China 3The University of Sydney, Australia 4West Virginia University, USA 5Northeastern University, USA 6Columbia University, USA 7University at Buffalo, USA |
| Pseudocode | Yes | Algorithm 1 Me Co Training. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of its source code. |
| Open Datasets | Yes | Datasets. We perform experiments on four standard datasets used in DFME literature, including MNIST, CIFAR10, CIFAR100 [25] and Mini Image Net [52] (100 classes). |
| Dataset Splits | Yes | Datasets. We perform experiments on four standard datasets used in DFME literature, including MNIST, CIFAR10, CIFAR100 [25] and Mini Image Net [52] (100 classes). |
| Hardware Specification | Yes | We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the A5000 used for this research. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | For decision-based DFME methods, following [47], we use a query budget of 10M for CIFAR100 and 8M for CIFAR-10. For score-based DFME methods, following [51], we set the number of queries to be 2M for MNIST, 20M for CIFAR10, and 200M for CIFAR100, respectively. We perform each experiment for 5 runs with a mean and standard deviation of results. We provide more implementation details in Appendix 7. |