Saudi Cultural Missions Theses & Dissertations
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Item Restricted Deepfake Technology: A Multi-Disciplinary Analysis of Technical, Social, Legal, and Ethical Issues(Saudi Digital Library, 2026) Alanazi, Sami; Asif, SeemalAdvances in deep learning have enabled the creation of highly realistic synthetic media capable of manipulating faces, voices, and actions with increasing fidelity. These techniques support legitimate uses in entertainment, education, and digital content production. At the same time, misuse has raised concerns linked to misinformation, privacy infringement, identity fraud, and ethical accountability. This thesis examines deepfake technology as a socio-technical problem, integrating technical analysis with social, legal, and ethical perspectives to inform effective mitigation. Three strands structure the investigation. A critical review of the literature establishes the state of generative and forensic research. Fourteen expert interviews across artificial intelligence, law, ethics, and cybersecurity provide institutional and professional context. Alongside this, empirical experimentation evaluates deep learning approaches to deepfake detection under realistic conditions. Initial technical work focused on image-based forensics, leading to the development of AI-Guard, a convolutional detection system designed for practical deployment. Evaluation results indicate that AI-Guard achieved 98% validation accuracy and 93.2% test accuracy on a dataset exceeding 450,000 images, while supporting real-time inference on mobile platforms. Video forensics forms the second technical contribution. Building on insights from earlier experiments, the thesis introduces VIDS-Guard, a forensics-aware detection framework that combines frequency-domain features, YCbCr colour decomposition, and temporal Transformer-based attention. Trained on 26,975 videos, the system achieved an accuracy of 0.91, a Macro-F1 score of 0.90, and an AUC of 0.97, outperforming established benchmark models across multiple evaluation settings. The results suggest that architectural diversity and temporal modelling improve generalisation under dataset variation. Technical findings do not stand alone. Analysis of expert perspectives and regulatory gaps indicates that detection systems, if unreliable or poorly governed, may themselves contribute to harm. Effective responses therefore appear to require coordination between deployable forensic tools, legal clarity, and ethical safeguards. By aligning system design with institutional and societal considerations, this thesis advances both practical detection capability and regulatory understanding, contributing to transparency, accountability, and resilience within the digital media ecosystem.10 0