Learning Self-distilled Features for Facial Deepfake Detection Using Visual Foundation Models: General Results and Demographic Analysis

Learning Self-distilled Features for Facial Deepfake Detection Using Visual Foundation Models: General Results and Demographic Analysis

Modern deepfake techniques produce highly realistic false media content with the potential for spreading harmful information, including fake news and incitements to violence. Deepfake detection methods aim to identify and counteract such content by employing machine learning algorithms, focusing ma...

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Detalles Bibliográficos
Main Authors: Yan Martins Braz Gurevitz Cunha, Bruno Rocha Gomes, José Matheus C. Boaro, Daniel de Sousa Moraes, Antonio José Grandson Busson, Julio Cesar Duarte, Sérgio Colcher
Formato: Artigo
Idioma:English
Publicado: Brazilian Computer Society 2024-07-01
Series:Journal on Interactive Systems
Subjects:
Deepfake Detection
Foundation Models
Machine Learning
Demographic Analysis
Self-Supervised Methods
Acceso en liña:https://journals-sol.sbc.org.br/index.php/jis/article/view/4120

Internet

https://journals-sol.sbc.org.br/index.php/jis/article/view/4120

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