ZeroMark: Towards Dataset Ownership Verification without Disclosing Watermark
Authors: Junfeng Guo, Yiming Li, Ruibo Chen, Yihan Wu, chenxi liu, Heng Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets verify the effectiveness of our Zero Mark and its resistance to potential adaptive attacks. |
| Researcher Affiliation | Academia | Junfeng Guo1, , Yiming Li2, , Ruibo Chen1, Yihan Wu1, Chenxi Liu1, Heng Huang1 1Department of Computer Science, Institute of Health Computing University of Maryland College Park 2College of Computing and Data Science, Nanyang Technology University {gjf2023,ruibo,yihanwu,chenxi,heng}@umd.edu; liyiming.tech@gmail.com |
| Pseudocode | Yes | Algorithm 1 The main process of our Zero Mark. |
| Open Source Code | Yes | The codes for reproducing our main experiments are publicly available at Git Hub. |
| Open Datasets | Yes | Currently, there are many (high-quality) public datasets, such as CIFAR [4] and Image Net [5], that can be easily downloaded and used. |
| Dataset Splits | Yes | CIFAR-10. CIFAR-10 dataset contains 10 labels, 50,000 training samples, and 10,000 validation samples. The training and validation samples are distributed evenly across each label. |
| Hardware Specification | Yes | We use six NVIDIA RTX 2080 Ti GPUs for performing experiments. |
| Software Dependencies | No | The paper mentions software like 'Adam optimizer [55]' and 'Backdoor Box2 [54]' but does not provide specific version numbers for general software dependencies (e.g., Python, PyTorch). |
| Experiment Setup | Yes | To train DNN models, we use Adam optimizer [55] with the initial learning rate as 0.01. |