# Explainable Label-flipping Attacks on Human Emotion Assessment System

arXiv: https://arxiv.org/abs/2302.04109 · HTML: https://mlsanity.com/paper/2302.04109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection](https://mlsanity.com/paper/1802.03041/) (2018)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals](https://mlsanity.com/paper/2301.06923/) (2023)

## Abstract (arXiv)

This paper's main goal is to provide an attacker's point of view on data poisoning assaults that use label-flipping during the training phase of systems that use electroencephalogram (EEG) signals to evaluate human emotion. To attack different machine learning classifiers such as Adaptive Boosting (AdaBoost) and Random Forest dedicated to the classification of 4 different human emotions using EEG signals, this paper proposes two scenarios of label-flipping methods. The results of the studies show that the proposed data poison attacksm based on label-flipping are successful regardless of the model, but different models show different degrees of resistance to the assaults. In addition, numerous Explainable Artificial Intelligence (XAI) techniques are used to explain the data poison attacks on EEG signal-based human emotion evaluation systems.