I. Sireesha, Ch. Sai Charan, M. Hemanth, B. Sandeep 03 03 The proliferation of GANgenerated images poses significant challenges to digital media trust This paper proposes a CNNbased approach for detecting AIgenerated images with Explainable AI integration The model leverages CNN feature extraction to identify subtle artifacts in synthetic images GradCAM and SHAP techniques provide visual and quantitative explanations revealing critical regions the model uses for classification Experiments demonstrate 953 accuracy in distinguishing real from AIgenerated images GradCAM visualizations confirm the model focuses on meaningful regions such as unnatural textures and generative artifacts Despite promising results limitations include vulnerability to adversarial examples and generalization challenges with novel GAN architectures The system is deployed as a Django web application enabling realtime image classification with explanations Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026