WFB:基于水标识的版权保护框架,用于通过区块链实现联合学习模型
Sujie Shao1, Yue Wang2, Chao Yang3
1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China. buptssj@bupt.edu.cn.
Scientific reports
|August 21, 2024
概括
联合学习模式容易受到攻击和版权问题. 本研究介绍了WFB,这是一个区块链框架,通过改进的水印来增强联合学习模型所有权验证和版权保护.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 区块链技术 区块链技术
背景情况:
- 联合学习 (FL) 训练模型在不共享私人数据的情况下进行协作,这对于对隐私敏感的行业至关重要.
- FL模型面临诸如对抗性攻击和所有权泛化等威胁,危及版权和可靠性.
- 目前用于FL所有权验证的模型水印缺乏提取的水印和验证之间的可靠联系,阻碍了闭环保护.
研究的目的:
- 提出WFB,一个区块链授权的水标框架,用于联合模型所有权验证.
- 解决FL目前的水印验证计划中的可信性问题.
- 为FL模型版权保护建立一个强大的闭环框架.
主要方法:
- 开发了一种改进的水标生成算法,以提高水标的可信度.
- 在联合学习过程中实施了水标嵌入方法.
- 利用区块链技术来安全和可靠地存储水标信息.
主要成果:
- 在验证FL模型所有权方面,WFB表现出高保真性,有效性和稳定性.
- 该框架通过水标真实性提高了所有权验证的可信度.
- 实验结果验证了拟议方法的性能和安全增强.
结论:
- WFB为联合学习提供了一个可信和闭环的水标框架.
- 区块链的整合提高了FL模型所有权验证的安全性,可追溯性和可靠性.
- 这种方法有效地保护FL模型版权免受各种威胁.
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