Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Hammadi, Ahmed Y. Al"'
Autor:
Zhang, Zhibo, Li, Pengfei, Hammadi, Ahmed Y. Al, Guo, Fusen, Damiani, Ernesto, Yeun, Chan Yeob
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. While EEG signal analysis has attracted att
Externí odkaz:
http://arxiv.org/abs/2401.01896
Advanced image tampering techniques are increasingly challenging the trustworthiness of multimedia, leading to the development of Image Manipulation Localization (IML). But what makes a good IML model? The answer lies in the way to capture artifacts.
Externí odkaz:
http://arxiv.org/abs/2307.14863
Autor:
Zhang, Zhibo, Umar, Sani, Hammadi, Ahmed Y. Al, Yoon, Sangyoung, Damiani, Ernesto, Yeun, Chan Yeob
Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention. However, EEG signal-based risk assessment systems, which could evaluate the emotional states of humans, have shown sev
Externí odkaz:
http://arxiv.org/abs/2302.04224
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 machin
Externí odkaz:
http://arxiv.org/abs/2302.04109
Autor:
Zhang, Zhibo, Umar, Sani, Hammadi, Ahmed Y. Al, Yoon, Sangyoung, Damiani, Ernesto, Ardagna, Claudio Agostino, Bena, Nicola, Yeun, Chan Yeob
Publikováno v:
IEEE Access 2023
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'
Externí odkaz:
http://arxiv.org/abs/2301.06923