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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Dettagli Bibliografici
Autori principali: 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
Natura: Articolo
Lingua:English
Pubblicazione: Brazilian Computer Society 2024-07-01
Serie:Journal on Interactive Systems
Soggetti:
Deepfake Detection
Foundation Models
Machine Learning
Demographic Analysis
Self-Supervised Methods
Accesso online:https://journals-sol.sbc.org.br/index.php/jis/article/view/4120

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

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