Design and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory

Autor:	Laor Boongasame, Jindaphon Boonpluk, Sunisa Soponmanee, Jirapond Muangprathub, Karanrat Thammarak
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Electronic computers. Computer science QA75.5-76.95
Zdroj:	Applied Computational Intelligence and Soft Computing, Vol 2024 (2024)
Druh dokumentu:	article
ISSN:	1687-9732
DOI:	10.1155/2024/8729440
Popis:	This study aims to design and implement deepfake video detection using VGG-16 in combination with long short-term memory (LSTM). In contrast to other studies, this study compares VGG-16, VGG-19, and the newest model, ResNet-101, including LSTM. All the models were tested using Celeb-DF video dataset. The result showed that the VGG-16 model with 15 epochs and 32 batch sizes had the highest performance. The results showed that the VGG-16 model with 15 epochs and 32 batch sizes exhibited the highest performance, with 96.25% accuracy, 93.04% recall, 99.20% specificity, and 99.07% precision. In conclusion, this model can be implemented practically.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/e42e5b26727e4f7bb23adce1c58ff516 Zobrazit plný text záznamu Plný text View record in DOAJ