论文标题
基于EEG的脑部计算机界面中的对抗伪影检测
Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces
论文作者
论文摘要
机器学习在基于脑电图（EEG）的脑部计算机界面（BCIS）方面取得了巨大成功。大多数现有的BCI研究都致力于提高其准确性，但很少有人认为其安全性。然而，最近的研究表明，基于EEG的BCI很容易受到对抗性攻击的影响，在该攻击中，在输入中增加的小扰动可能会导致错误分类。对抗性例子的检测对于对这种现象和防御的理解至关重要。本文首次探讨了基于EEG的BCIS中的对抗性检测。使用三个卷积神经网络在两个EEG数据集上进行了实验，以验证多种检测方法的性能。我们证明可以检测到白色框和黑框攻击，前者更容易检测到。
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but few had considered its security. Recent studies, however, have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detection of adversarial examples is crucial to both the understanding of this phenomenon and the defense. This paper, for the first time, explores adversarial detection in EEG-based BCIs. Experiments on two EEG datasets using three convolutional neural networks were performed to verify the performances of multiple detection approaches. We showed that both white-box and black-box attacks can be detected, and the former are easier to detect.
