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在基于EEG的脑电脑接口中检测敌对的工件.

Xiaoqing Chen1, Lubin Meng1, Yifan Xu1

  • 1Belt and Road Joint Laboratory on Measurement and Control Technology, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Journal of neural engineering
|October 21, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于检测基于脑电图 (EEG) 的脑电脑接口 (BCI) 的对抗性攻击的新方法. 我们的方法通过实现对常见攻击的近乎完美的检测率来显著提高安全性.

关键词:
这是一个EEGEEGEEGEEGEEGEEGEEG.敌对的文物检测检测对抗性的文物检测.敌对攻击是对抗性的攻击.大脑 计算机接口

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 计算机科学 计算机科学

背景情况:

  • 机器学习已经推进了基于脑电图 (EEG) 的脑电脑接口 (BCI),但这些系统容易受到对抗性攻击.
  • 通过微妙的输入扰动创建的对抗性示例可以导致错误分类,构成安全风险.
  • 检测这些对抗性例子对于理解BCI漏洞和开发强大的防御至关重要.

研究的目的:

  • 这项研究是第一个专门在基于EEG的BCI中探索对抗检测的研究.
  • 该研究旨在评估和提出新的方法来识别BCI系统中的对抗性示例.
  • 关键目标包括评估针对各种攻击的检测性能,并了解检测器的可转移性.

主要方法:

  • 从计算机视觉调整了流行的对抗性检测技术,用于BCI应用.
  • 开发了两种新的Mahalanobis基于距离的检测方法.
  • 提出了三种新的基于共弦距离的探测方法.
  • 在三个EEG数据集,三个神经网络和四种对抗性攻击类型中评估了八种检测方法.

主要成果:

  • 在检测白盒对抗性攻击方面,实现了高达99.99%的曲线下面区域 (AUC) 得分.
  • 展示了拟议的Mahalanobis和cosine基于距离的探测器的有希望的性能.
  • 评估了对抗性检测器对未知攻击类型的可转移性,揭示了对抗性示例之间的明显分布.

结论:

  • 可以有效地检测对EEG-BCI的白盒对抗性攻击.
  • 对抗性样本分布的差异需要定制的检测策略.
  • 这项工作为增强未来BCI模型的安全性和可靠性提供了基础的见解.