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Found 5 papers, 1 papers with code
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning.
IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer
To bridge this gap, based on the fact that artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision that could converge with a small amount of data.
Explainable Label-flipping Attacks on Human Emotion Assessment System
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.
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems
Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention.
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
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.
