Model Synthesis for Zero-Shot Model Attribution
文献类型：期刊论文
| 作者 | Yang, Tianyun2,3; Wang, Danding2,3; Cao, Juan2,3; Xu, Chang1 |
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2025 |
| 卷号 | 27页码:8967-8980 |
| 关键词 | Fingerprint recognition Adaptation models Training Data models Analytical models Computational modeling Convolution Accuracy Training data Synthetic data Model fingerprint model attribution model synthesis zero-shot adaptation |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2025.3607778 |
| 英文摘要 | Nowadays, generative models are shaping various fields such as art, design, and human-computer interaction, yet they are accompanied by copyright infringement and content management challenges. In response, existing research seeks to identify the unique fingerprints on the images they generate, which can be leveraged to attribute the generated images to their source models. However, existing methods are restricted to identifying models within a static set included in classifier training, incapable of adapting dynamically to newly emerging unseen models. To bridge this gap, this paper aims to develop a generalized model fingerprint extractor capable of zero-shot attribution that effectively attributes unseen models without exposure during training. Central to our method is a model synthesis technique, which generates numerous synthetic models that mimic the fingerprint patterns of real-world generative models. The design of the synthesis technique is motivated by observations on how the basic generative model's architecture building blocks and parameters influence fingerprint patterns, and it is validated through designed metrics to examine synthetic models' fidelity. Our experiments demonstrate that the fingerprint extractor, trained solely on synthetic models, achieves impressive zero-shot generalization on a wide range of real-world generative models, improving model identification and verification accuracy on unseen models by over 40% and 15%, respectively, compared to existing approaches. |
| 资助项目 | Innovation Funding of ICT, CAS[E561160] ; Beijing Science and Technology Plan Project[Z241100001324018] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001652357700001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42885]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Cao, Juan |
| 作者单位 | 1.Univ Sydney, Fac Engn, Sch Comp Sci, Sydney, NSW 2050, Australia 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Tianyun,Wang, Danding,Cao, Juan,et al. Model Synthesis for Zero-Shot Model Attribution[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2025,27:8967-8980. |
| APA | Yang, Tianyun,Wang, Danding,Cao, Juan,&Xu, Chang.(2025).Model Synthesis for Zero-Shot Model Attribution.IEEE TRANSACTIONS ON MULTIMEDIA,27,8967-8980. |
| MLA | Yang, Tianyun,et al."Model Synthesis for Zero-Shot Model Attribution".IEEE TRANSACTIONS ON MULTIMEDIA 27(2025):8967-8980. |
入库方式： OAI收割
来源：计算技术研究所
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