在没有监督的领域中探索后门攻击,以确保心脏MRI基础的诊断
Xiaofeng Liu1,2, Fangxu Xing1, Hanna Gaggin3
1Dept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Proceedings. IEEE International Symposium on Biomedical Imaging
|October 18, 2024
概括
本研究介绍了在无监督域调整 (OSUDA) 中对后门攻击的防御. 拟议的方法量化后门的敏感性,并覆盖恶意内核,保护目标模型免受受损害的现成模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 无监督域调整 (OSUDA) 保护敏感的源数据,但有可能从受损的模型中传播后门.
- 在OSUDA中的现成模型容易受到后门攻击,可能感染目标域模型.
- 现有的OSUDA方法缺乏对复杂的后门攻击的防御.
研究的目的:
- 在无监督域适应 (OSUDA) 中开发强大的防御机制来抵御后门攻击.
- 在OSUDA期间量化和减轻道智能内核的后门敏感性.
- 确保准确的伪标签,并保护目标模型免受恶意数据中毒.
主要方法:
- 使用Lipschitz常量量化通道智能的后门灵敏度.
- 通过随机初始化覆盖与后门相关的通道内核来消除后门感染.
- 使用一个辅助模型和一个完整的源代码模型来准确的伪标签与清洁的目标数据.
主要成果:
- 证明了目标模型在OSUDA期间容易受到后门攻击.
- 验证了拟议的防御机制在缓解后门感染方面的有效性.
- 展示了框架在多中心,多供应商和多疾病心脏数据集上的表现.
结论:
- 欧苏达对来自受损的源代码模型的后门攻击存在重大漏洞.
- 拟议的防御有效地通过准和重置恶意通道内核来中和后门感染.
- 这项工作提高了在医疗成像等敏感应用中域调整的安全性和可靠性.
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