Deepfake Detection Using a Fusion of Convolutional Neural Networks and Vision Transformers

Griffiths Kelly, Alana Ann Beatrice (2025) Deepfake Detection Using a Fusion of Convolutional Neural Networks and Vision Transformers. Masters thesis, Dublin, National College of Ireland.

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Abstract

Deepfakes present a growing threat to digital media authenticity and cybersecurity, with existing detection models often struggling to generalise across datasets or withstand real-world distortions. This study develops a hybrid Deepfake detection pipeline that combines Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to better capture both local visual artefacts and global structural inconsistencies. A complete preprocessing workflow was implemented, including video frame extraction, face detection using MTCNN, and image normalisation. The proposed model fuses EfficientNet-B0 and ViT-B/16 feature embeddings to perform frame-level classification, with video-level predictions derived from aggregated softmax probabilities. Evaluation on real video footage demonstrates that the hybrid architecture is functional, reproducible, and capable of producing stable and interpretable Deepfake likelihood scores despite not being trained on Deepfake-specific datasets. The study highlights the complementary strengths of CNN and transformer-based features and identifies key limitations such as lack of temporal modelling and dataset-specific training. The findings provide a foundation for future research into multimodal, temporally-aware, and more robust Deepfake detection systems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2026 11:07
Last Modified:	03 Sep 2026 11:07
URI:	https://norma.ncirl.ie/id/eprint/9799

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