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对freeloader说不:保护您的深度模型的知识产权
IEEE transactions on pattern analysis and machine intelligence
|August 26, 2024
概括
保护模型的知识产权 (IP) 是至关重要的. 新的紧型不可转让的金字塔隔离域 (CUPI-Domain) 通过隔离独特的风格特征来防止未经授权的模型转移.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习安全 机器学习安全
背景情况:
- 模型知识产权 (IP) 保护对于保护计算资源和知识劳动越来越重要.
- 确保模型培训师和所有者的知识产权安全需要强大的所有权验证和授权机制.
研究的目的:
- 引入一种新的方法,即紧的不可转让的金字塔隔离域 (CUPI-Domain),以防止未经授权的训练模型转移.
- 提高机器学习模型对知识产权盗窃的安全性.
主要方法:
- 开发CUPI-Domain生成器,将授权和CUPI-Domains的风格和语义特征融合在一起.
- 实现外部域信息存储库 (DIMB),用于存储和更新域特征.
- 新风格和歧视性损失功能的设计,以增强特征区分.
主要成果:
- CUPI-Domain实际上是防止非法模型转让的障碍.
- 实验证明了这种方法在各种公共数据集和骨干模型中的有效性.
- 提出的方法成功地提高了授权和未授权域名之间的区别.
结论:
- CUPI-Domain提供了一种高效有效的解决方案,用于模范知识产权保护.
- 该方法提供了针对目标指定的和无目标的CUPI-Domain选项的灵活性.
- 这项研究在确保机器学习模型的安全方面取得了重大进展.
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