Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
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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 results demonstrate the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verify 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, and also the first study on UAPs for target attacks. UAPs are easy to construct, and can attack BCIs in real-time, exposing a critical security concern of BCIs.
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