针对第三方阿尔茨海默氏症诊断系统的通用黑子攻击
Claudio Sebastián Sigvard1, José M Franco2, Germán Mato1,3,4
1Departamento Física Médica, Centro Atómico Bariloche, Argentina.
Biomedical physics & engineering express
|November 5, 2025
概括
像VolBrain这样的医疗AI诊断工具容易受到敌对攻击. 即使没有系统访问,这些攻击也可以降低AI性能,突出显示临床使用的风险和需要更好的AI安全性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 人工智能 (AI) 在医学成像中越来越多地用于疾病诊断.
- 人工智能在临床环境中的部署受到其对敌对攻击的脆弱性所阻碍.
研究的目的:
- 系统地评估VolBrain神经成像诊断平台对通用黑子对抗攻击的易感性.
- 评估不同对抗性攻击方法的有效性,包括FGSM和DeepFool,降低VolBrain的诊断性能.
主要方法:
- 使用在不同的数据集和架构上训练的替代卷积神经网络 (CNN) 生成对抗性扰动.
- 采用快速渐变信号方法 (FGSM) 和DeepFool攻击来创建通用黑子干扰.
- 评估了这些干扰对VolBrain诊断性能的影响.
主要成果:
- 敌对的干扰可靠地降低了VolBrain的诊断性能.
- 基于DeepFool的攻击对可比的扰动大小特别有效.
- 平均攻击方法也证明了有效性,表明对通用攻击的脆弱性.
结论:
- 即使没有内部系统知识,VolBrain也容易受到普遍的黑子对抗性攻击.
- 这些发现强调了这种攻击对医疗AI构成的重大风险.
- 迫切需要强大的防御机制,并进一步研究医疗人工智能系统的对抗性强度.
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