基于频域注入的联合学习后门攻击
Jiawang Liu1, Changgen Peng1, Weijie Tan1,2
1State Key Laboratory of Public Big Data, College of Compute Science and Technology, Guizhou University, Guiyang 550025, China.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
这项研究引入了一种新的频域后门攻击,用于联合学习 (FL). 新方法比现有攻击更隐蔽,更有效,保护分布式机器学习中的全球模型.
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 机器学习
背景情况:
- 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
- FL系统容易受到恶意客户端的后门攻击.
- 现有的攻击通常使用可见触发器并破坏语义信息.
研究的目的:
- 为联合学习提出一种新的,更隐蔽的后门攻击.
- 克服现有的空间域攻击的局限性.
- 为了提高后门攻击在FL的有效性.
主要方法:
- 开发了一种基于频域注入的后门攻击.
- 利用里埃转换来混合频域中的触发器和清洁图像.
- 在保留语义内容的同时注入低频触发信息.
主要成果:
- 拟议的攻击比现有方法更隐蔽.
- 攻击在FL场景中显示出更高的有效性.
- 在多个图像分类数据集上进行了实验.
结论:
- 频域攻击提供了一个更强大的方法来插入后门在FL.
- 这种方法保留了语义信息,使得攻击更难被检测.
- 这些发现突出了分布式机器学习的新安全挑战.
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