Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces

Jiyoung Jung, Hee Joon Moon, Geunhyeok Yu, Hyoseok Hwang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality.

Original languageEnglish
Pages (from-to)5622-5633
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume27
Issue number11
DOIs
StatePublished - 1 Nov 2023

Keywords

  • Adversarial attack
  • EEG classification
  • brain computer interfaces
  • universal adversarial perturbation

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