One Model for Many Fakes: Detecting GAN and Diffusion-Generated Forgeries in Faces, Invoices, and Medical Heterogeneous Data

The rapid advancement of generative models, such as GAN and diffusion architectures, has enabled the creation of highly realistic forged images, raising critical challenges in key domains. Detecting such forgeries is essential to prevent potential misuse in sensitive areas, including healthcare, financial documentation, and identity verification. This study addresses the problem by deploying a vision transformer (ViT)-based multiclass classification framework to identify image forgeries across three distinct domains: invoices, human faces, and medical images. The dataset comprises both authentic and AI-generated samples, creating a total of six classification categories. To ensure uniform feature representation across heterogeneous data and to effectively utilize pretrained weights, all images were resized to 224 × 224 pixels and converted to three channels. Model training was conducted using stratified K-fold cross-validation to maintain balanced class distribution in each fold. Experimental results of this study demonstrate consistently high performance across three folds, with an average training accuracy of 0.9983 (99.83%), validation accuracy of 0.9620 (96.20%), and test accuracy of 0.9608 (96.08%), along with a weighted F1 score of 0.9608 and exceeding 0.96 (96%) for all classes. These findings highlight the effectiveness of ViT architectures for cross-domain forgery detection and emphasize the importance of preprocessing standardization when working with mixed datasets.