From Deception to Detection: A Cybersecurity-Driven Approach to Deepfake Identification

Nagaraj Kumar, Sharan (2025) From Deception to Detection: A Cybersecurity-Driven Approach to Deepfake Identification. Masters thesis, Dublin, National College of Ireland.

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Abstract

The usage of deepfake technology has increased rapidly over the years, posing serious threats to businesses and people who are affected. It facilitates activities such as identity fraud, social engineering attacks, and misinformation, causing loss of reputation. This research work proposed a hybrid Deep Learning model that integrates Convolutional Neural Network, EfficientNet, and Vision Transformers to detect deepfake images. A custom dataset that includes a partial amount of the benchmarked public dataset (FaceForensics++, DFDC, and Celeb-DF-V2) and images from Google searches to cover the latest manipulated images is used to train the model. Hybrid architecture was validated against the established detection models, demonstrating an accuracy up to 93% and an Area Under the Curve (AUC) of 97.81%. Strengthening detection models and addressing challenges related to datasets like bias and high-quality data forgery were the focus of this work. This research work contributes to building more robust cybersecurity measures with the help of Artificial Intelligence to safeguard digital media integrity.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Prior, Michael UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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:	16 Jun 2026 13:42
Last Modified:	16 Jun 2026 13:42
URI:	https://norma.ncirl.ie/id/eprint/9363

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