mlsanityData to 14 Oct 2026

Explainable Label-flipping Attacks on Human Emotion Assessment System

arXiv 2302.04109 ↗First posted 8 Feb 2023cs.LGcited by 0

Not on the map. Why?

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.

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

3 of 7 references.

References · 3

  1. 2018Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detectioncited by 46
  2. 2022Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Researchcited by 25
  3. 2023Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signalscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2022Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System
  2. 2023Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
  3. 2024Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis
  4. 2022A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition
  5. 2022New data poison attacks on machine learning classifiers for mobile exfiltration
  6. 2024Active Poisoning: Efficient Backdoor Attacks on Transfer Learning-Based Brain-Computer Interfaces ↗
  7. 2024Professor X: Manipulating EEG BCI with Invisible and Robust Backdoor Attack ↗
  8. 2023A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition