Explainable Deep Learning Framework for AI-Generated Image Detection Using NASNet and Grad-CAM
TL;DR
This project presents an explainable deep learning framework for identifying real and AI-generated images using the NASNet architecture and achieves high detection accuracy while providing interpretable visual explanations, making it suitable for digital image verification, media authentication, and cybersecurity applications.
Abstract
The rapid growth of generative artificial intelligence has made it easier to create highly realistic synthetic images, increasing the risk of misinformation, identity misuse, and digital fraud. Distinguishing AI-generated images from authentic ones has become a significant challenge due to their visual similarity. This project presents an explainable deep learning framework for identifying real and AI-generated images using the NASNet architecture. The model is trained on a balanced dataset containing genuine and synthetic face images after applying preprocessing techniques such as resizing, normalization, and data shuffling. To improve transparency, the system integrates Explainable AI (XAI) methods, including Grad-CAM and LIME, which highlight the image regions that influence the model’s predictions. A web-based interface enables users to upload images in different formats and receive instant classification results. Experimental evaluation demonstrates that the proposed approach achieves high detection accuracy while providing interpretable visual explanations, making it suitable for digital image verification, media authentication, and cybersecurity applications.