English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning has made significant breakthroughs in many fields, includingelectroencephalogram (EEG) based brain-computer interfaces (BCIs). However,deep learning models are vulnerable to adversarial attacks, in whichdeliberately designed small perturbations are added to the benign input samplesto fool the deep learning model and degrade its performance. This paperconsiders transferability-based black-box attacks, where the attacker trains asubstitute model to approximate the target model, and then generatesadversarial examples from the substitute model to attack the target model.Learning a good substitute model is critical to the success of these attacks,but it requires a large number of queries to the target model. We propose anovel framework which uses query synthesis based active learning to improve thequery efficiency in training the substitute model. Experiments on threeconvolutional neural network (CNN) classifiers and three EEG datasetsdemonstrated that our method can improve the attack success rate with the samenumber of queries, or, in other words, our method requires fewer queries toachieve a desired attack performance. To our knowledge, this is the first workthat integrates active learning and adversarial attacks for EEG-based BCIs.

Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces