Optimizing deepfake detection using deep convolutional neural networks for real-time detection

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dc.contributor.authorBacungan, Ezekiel O.
dc.date.accessioned2026-09-18T05:44:03Z
dc.date.available2026-09-18T05:44:03Z
dc.date.issued2023-05
dc.descriptionAbstract Only
dc.description.abstractIn a world where there is such an abundance of video and image materials that are consumed every day on social media, the proliferation of falsified media is causing an increasing alarm. To detect deepfakes using deep convolutional networks, a quasi-experimental research design was adapted to determine the preprocessing techniques, convolutional neural networks, and model that best perform in detecting deepfakes. Datasets were gathered from publicly available resources, then these datasets were pre-processed and used in training and testing the proposed model. Finally, the trained model’s performance was evaluated in terms of accuracy, precision, recall, f1-score, and speed in detecting deepfake images. By configuring these methods and architecture, the researcher determined which setting would be best in detecting deepfake images. It was found that in preprocessing datasets for deepfake detection, data normalization, percentage splitting, inclusion or exclusion of data augmentation improves the training progress while randomization is avoided. On the other hand, the use of base models with deep and great number of layers and connections improves performance in classifying deepfakes.
dc.identifier.citationBacungan, E.O. (2023). Optimizing deepfake detection using deep convolutional neural networks for real-time detection. [Unpublished Master's Thesis]. Don Mariano Marcos Memorial State University – South La Union Campus, Agoo, La Union. Lakasa ti Sirib, DMMMSU Institutional Repository.
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2618
dc.language.isoen
dc.publisherDon Mariano Marcos Memorial State University - South La Union Campus
dc.sdgSDG 9
dc.titleOptimizing deepfake detection using deep convolutional neural networks for real-time detection
dc.typeThesis
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