In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

In-The-Wild deepfake detection using adaptable CNN models with visual class activation mapping for improved accuracy

Deepfake technology has become increasingly sophisticated in recent years, making detecting fake images and videos challenging. This paper investigates the performance of adaptable convolutional neural network (CNN) models for detecting Deepfakes. In-the-wild OpenForensics dataset was used to evalua...

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Detalles Bibliográficos
Autores principales: Muhammad Salihin, Saealal, Mohd Zamri, Ibrahim, Mohd Ibrahim, Shapiai, Norasyikin, Fadilah
Formato: Conference or Workshop Item
Lenguaje:English
English
Publicado: 2023
Materias:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Acceso en línea:http://umpir.ump.edu.my/id/eprint/38714/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models.pdf
http://umpir.ump.edu.my/id/eprint/38714/2/In-The-Wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy_ABS.pdf

Internet

http://umpir.ump.edu.my/id/eprint/38714/1/In-the-wild%20deepfake%20detection%20using%20adaptable%20CNN%20models.pdf
http://umpir.ump.edu.my/id/eprint/38714/2/In-The-Wild%20deepfake%20detection%20using%20adaptable%20CNN%20models%20with%20visual%20class%20activation%20mapping%20for%20improved%20accuracy_ABS.pdf

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