A Pre-trained Visual Geometry Group-16 Model for the Identification of Fake Images

This research presents a method for identifying real and AI-generated (fake) images using the VGG16 model, a widely recognized convolutional neural network. Our approach leverages the powerful feature extraction capabilities of VGG16, combined with additional custom layers, to effectively distinguish between authentic and synthetic images. The model was trained and validated on a dataset consisting of both real and AI-generated images. Throughout the training process, we observed a consistent improvement in training and validation accuracy, with training accuracy reaching 99.49% and validation accuracy peaking at 96.40% by epoch 19. Despite minor fluctuations in validation loss, the model demonstrated robust performance and strong generalization capabilities. The results indicate that the VGG16-based model is highly effective in detecting AI-generated images, making it a valuable tool for applications requiring reliable image authenticity verification. Our findings underscore the potential of advanced deep learning techniques in enhancing digital image forensics, contributing to the broader field of AI and cybersecurity. This research highlights the importance of continual model refinement to address challenges such as overfitting and validation variability, ensuring sustained accuracy and reliability in real-world applications.