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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Detalhes bibliográficos
Principais autores: Muhammad Salihin, Saealal, Mohd Zamri, Ibrahim, Mohd Ibrahim, Shapiai, Norasyikin, Fadilah
Formato: Conference or Workshop Item
Idioma:English
English
Publicado em: 2023
Assuntos:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Acesso em linha: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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