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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מידע ביבליוגרפי
Main Authors:	Muhammad Salihin, Saealal, Mohd Zamri, Ibrahim, Mohd Ibrahim, Shapiai, Norasyikin, Fadilah
פורמט:	Conference or Workshop Item
שפה:	English English
יצא לאור:	2023
נושאים:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
גישה מקוונת:	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

אינטרנט

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

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

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