Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs

3 Dec 2019Zihan LiuXiao ZhangLubin MengDongrui Wu

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... (read more)

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