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...

पूर्ण विवरण

ग्रंथसूची विवरण
मुख्य लेखकों:	Muhammad Salihin, Saealal, Mohd Zamri, Ibrahim, Mohd Ibrahim, Shapiai, Norasyikin, Fadilah
स्वरूप:	Conference or Workshop Item
भाषा:	English English
प्रकाशित:	2023
विषय:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
ऑनलाइन पहुंच:	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

इंटरनेट

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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