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Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
[article]
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
arXiv:1912.01171v5
fatcat:ei5uy3c7yjc3tibcpd2w36zjla