| Autor: |
Zhang, Zhibo, Umar, Sani, Hammadi, Ahmed Y. Al, Yoon, Sangyoung, Damiani, Ernesto, Ardagna, Claudio Agostino, Bena, Nicola, Yeun, Chan Yeob |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access 2023 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ACCESS.2023.3245813 |
| Popis: |
The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods, including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
