Abstract
Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combination of Facfo-rensic++ and DeepFake Detection Challenge Dataset. This proposed system gives the highest accuracy of 97%.
Cite
CITATION STYLE
S., G., S., S., T., N., K., S. S., & S., S. (2023). Deep fake detection using deep learning techniques. Applied and Computational Engineering, 2(1), 232–241. https://doi.org/10.54254/2755-2721/2/20220655
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