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

Boongasame, Laor; Boonpluk, Jindaphon; Soponmanee, Sunisa; Muangprathub, Jirapond; Thammarak, Karanrat

doi:10.1155/2024/8729440

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

Author(s)

Boongasame, Laor

Boonpluk, Jindaphon

Soponmanee, Sunisa

Muangprathub, Jirapond

Thammarak, Karanrat

Date Issued

January 1, 2024

Type

Article

DOI

10.1155/2024/8729440

Abstract

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.

Citation

Applied Computational Intelligence and Soft Computing, 2024, 2024