LatAtk:一种医疗图像攻击方法,专注于具有高可转移性的损伤区域
Long Li1,2, Yibo Huang1,3, Chong Li3
1Joint International Research Laboratory of Spatio-Temporal Information and Intelligent Location Services, Guilin University of Electronic Technology, Guilin 541004, China.
Journal of imaging
|November 26, 2025
概括
研究人员开发了LatAtk,这是一种用于医疗图像的新型对抗性攻击方法. 该方法针对病变区域,以提高医疗保健应用中的深度神经网络 (DNN) 安全性和可靠性.
科学领域:
- 医疗成像医学成像
- 深度学习安全 深度学习安全
- 敌对的攻击 敌对的攻击
背景情况:
- 人们越来越担心敏感医疗保健应用中的深度学习模型的安全性和可靠性.
- 敌对攻击对深度神经网络 (DNN) 的完整性构成重大威胁,特别是在医学成像中.
- 现有的攻击方法缺乏足够的可转移性和隐藏性,需要先进的安全解决方案.
研究的目的:
- 提出一种新的对抗性攻击方法,LatAtk,专门为医疗图像设计.
- 提高用于生命和健康安全应用中的DNN的安全性和可控性.
- 为了提高对抗对医疗图像分析的攻击的可转移性和隐藏性.
主要方法:
- LatAtk使用图像细分来区分医疗图像中的可攻击 (损伤) 和不可攻击区域.
- 干扰被战略性地注入到病变区域,以破坏DNN注意力机制.
- 使用类激活损失函数 (梯度加权类激活映射) 和纹理特征损失函数 (局部二进制模式) 来增强可转移性和保护图像完整性.
主要成果:
- 与现有方法相比,LatAtk在破坏DNN方面表现出更强的攻击性.
- 拟议的方法具有很高的可转移性,有效地攻击不同的DNN模型.
- LatAtk实现了更好的隐藏,保留了基本的纹理特征,并降低了对抗样本的检测能力.
结论:
- LatAtk在医学成像对抗性攻击方法方面取得了重大进展.
- 该技术在关键医疗保健场景中为DNN提供了增强的安全性和可靠性.
- 对LatAtk的进一步研究可以推动医疗AI强大的防御机制的创新.
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