Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs [article]

Zihan Liu and Lubin Meng and Xiao Zhang and Weili Fang and Dongrui Wu
2021 arXiv   pre-print
Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs. Experimental
more » ... demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs.
arXiv:1912.01171v5 fatcat:ei5uy3c7yjc3tibcpd2w36zjla