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

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dc.contributor.authorBoongasame, Laor
dc.contributor.authorBoonpluk, Jindaphon
dc.contributor.authorSoponmanee, Sunisa
dc.contributor.authorMuangprathub, Jirapond
dc.contributor.authorThammarak, Karanrat
dc.date.accessioned2026-08-06T10:44:15Z
dc.date.available2026-08-06T10:44:15Z
dc.date.issued2024-01-01
dc.description.abstractThis 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.
dc.identifier.citationApplied Computational Intelligence and Soft Computing, 2024, 2024
dc.identifier.doi10.1155/2024/8729440
dc.identifier.issn16879724
dc.identifier.other2-s2.0-85198085087
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15150
dc.sourceApplied Computational Intelligence and Soft Computing
dc.titleDesign and Implement Deepfake Video Detection Using VGG-16 and Long Short-Term Memory
dc.typeArticle

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