论文标题
对抗性刺激：通过扰动的感觉事件攻击脑部计算机界面
Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events
论文作者
论文摘要
已知机器学习模型容易受到输入域中对抗性扰动的影响，从而导致不正确的预测。受到这种现象的启发，我们探索了操纵基于脑电图的运动成像（MI）大脑计算机接口（BCIS）的可行性，这是通过感觉刺激的扰动。与对抗性示例类似，这些\ emph {对抗性刺激}旨在利用BCI系统的综合脑传感器处理组件的局限性，以处理参与者对感官刺激变化的响应的转变。本文提出，对抗性刺激是针对BCIS的攻击向量，并报告了初步实验对视觉对抗刺激对基于EEG基于EEG的MI BCIS完整性的影响的发现。我们的发现表明，较小的对抗刺激可以显着恶化所有参与者的Mi BCIS（p = 0.0003）。此外，我们的结果表明，这种攻击在诱发压力的条件下更有效。
Machine learning models are known to be vulnerable to adversarial perturbations in the input domain, causing incorrect predictions. Inspired by this phenomenon, we explore the feasibility of manipulating EEG-based Motor Imagery (MI) Brain Computer Interfaces (BCIs) via perturbations in sensory stimuli. Similar to adversarial examples, these \emph{adversarial stimuli} aim to exploit the limitations of the integrated brain-sensor-processing components of the BCI system in handling shifts in participants' response to changes in sensory stimuli. This paper proposes adversarial stimuli as an attack vector against BCIs, and reports the findings of preliminary experiments on the impact of visual adversarial stimuli on the integrity of EEG-based MI BCIs. Our findings suggest that minor adversarial stimuli can significantly deteriorate the performance of MI BCIs across all participants (p=0.0003). Additionally, our results indicate that such attacks are more effective in conditions with induced stress.
