为机器学习模型确定适当的知识产权保护机制:水印,指纹,模型访问和攻击的系统化
概括
对于复杂的机器学习 (ML) 模型来说,保护知识产权 (IP) 是至关重要的. 本研究为ML知识产权保护 (IPP) 提供了威胁,攻击和防御的统一分类.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 机器学习 (ML) 模型越来越复杂,训练成本也越来越高.
- 训练有素的机器学习模型的知识产权 (IP) 保护是一个日益关注的问题.
- 关于ML IP保护的现有研究是分散的,缺乏统一的观点和分类.
研究的目的:
- 在机器学习 (ML) 中对知识产权保护 (IPP) 的发现进行系统化.
- 专注于识别的威胁,攻击,并为ML模型提出防御方案.
- 为MLIPP开发一个全面的威胁模型和统一的分类学.
主要方法:
- 对MLIPP的现有研究进行系统审查和综合.
- 开发一个全面的ML知识产权威胁模型.
- 在统一的分类系统中对攻击和防御进行分类.
主要成果:
- 确定了ML IP保护的分散的研究环境.
- 在ML中开发了一种针对IP的综合威胁模型.
- 建立了一个统一的分类系统,对ML IP攻击和防御进行分类.
结论:
- 统一的分类学对于推动ML知识产权保护研究至关重要.
- 将ML和安全社区之间的研究联系起来,对于强大的IPP至关重要.
- 开发的威胁模型和分类结构为未来在ML安全方面的工作提供了基础.
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