计算机科学
数字水印
水印
方案(数学)
嵌入
知识产权
开源
财产(哲学)
数据挖掘
普遍性(动力系统)
人工智能
图像(数学)
软件
数学
操作系统
数学分析
哲学
认识论
物理
量子力学
作者
Lu Yang,Zongwei Tang,Xiuli Chai,Mingxu Wang,Shiping Song
出处
期刊:Optik
[Elsevier]
日期:2022-09-07
卷期号:269: 169931-169931
被引量:3
标识
DOI:10.1016/j.ijleo.2022.169931
摘要
The high quality datasets are an important guarantee for the rapid development of deep learning, so the protection of datasets should be taken seriously. The semi-open source datasets are the datasets that are free for some users and used for academic communication and are charged for another part of users and generates commercial value. However, most of the existing datasets protection mechanisms are only a single passive copyright verification mechanism, which cannot protect semi-open source datasets well. Based on this situation, this paper proposes a hierarchical protection scheme for the intellectual property of semi-open source datasets based on double watermarking. The scheme adopts different protection schemes for datasets for the above mentioned user groups: internal users and purchased users, to achieve the purpose of hierarchical protection. The passive verification and active protection functions for semi-open source datasets are realized by embedding blind watermark and visual watermark, respectively. Finally, the paper conducts experiments on a number of underlying datasets to verify the imperceptibility, universality, harmlessness and effectiveness of the scheme.
科研通智能强力驱动
Strongly Powered by AbleSci AI