Supervisor
Dr Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
The advancement of generative AI technologies has made it increasingly difficult to distinguish synthetic images from authentic ones. This capstone project addresses the challenge by developing a binary image classification model using deep learning techniques to differentiate AI-generated images from real photographs. Guided by the CRISP-DM methodology, we employed the DeepGuardDB dataset, consisting of 13,000 balanced image samples, evenly split between real and synthetic sources. We implemented and compared three Convolutional Neural Network (CNN) architectures through transfer learning, standardising input pipelines and integrating custom classification heads. Following a performance evaluation across multiple metrics, the best-performing model was selected for further optimisation using established techniques such as hyperparameter tuning and fine-tuning. This project demonstrates how deep learning can contribute to maintaining information integrity in media, journalism, and digital content platforms.
either AI-generated or real, addressing the growing challenge of synthetic media detection. Using the DeepGuardDB dataset and guided by the CRISP-DM methodology, we implemented and compared three Convolutional Neural Networks (CNNs) architectures via transfer learning. The best-performing model was further optimised using hyperparameter tuning and fine-tuning techniques The resulting model achieved strong accuracy and generalisation, making it a promising candidate for real-time deployment and practical use across diverse industries.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Gandara, B., & Varela, I.
(2025) Development of a Deep Learning Model for Synthetic vs. Real Image Classification Synthetic vs. Real image classification CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.46