Development of a Deep Learning Model for Synthetic vs. Real Image Classification Synthetic vs. Real image classification
Abstract
This project develops a deep learning model to classify images as 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.
This paper has been withdrawn.