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AI-Generated Image Detection with CNN and Interpretation Using Explainable AI
The proliferation of GAN-generated images poses significant challenges to digital media trust. This paper proposes a CNN-based approach for detecting AI-generated images with Explainable AI integration. The model leverages CNN feature extraction to identify subtle artifacts in synthetic images. Grad-CAM and SHAP techniques provide visual and quantitative explanations revealing critical regions the model uses for classification. Experiments demonstrate 95.3% accuracy in distinguishing real from AI-generated images. Grad-CAM 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 real-time image classification with explanations.
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