A Robust Deep Learning Framework for Detecting Real and AI-Generated Images Using Multi-Generator and Multi-Scale Feature Analysis

Abstract:- The rapid advancement of generative models such as GANs, autoencoders, and diffusion architectures has significantly increased the realism of synthetic images, creating challenges for reliable real-versus-fake image classification. This research proposes a robust deep learning framework capable of generalizing across multiple AI image generators while accurately distinguishing real images from synthetic content. To address existing research gaps—limited cross-generator generalization, insufficient fine-grained artifact detection, and lack of real-world distortions—a unified and diverse dataset was constructed by integrating real images, DeepFake Detection (DFDC) data, StyleGAN-generated images, ProGAN/PGGAN outputs, and Stable Diffusion synthetic images sourced from Kaggle. All images were standardized and augmented with real-world distortions such as compression artifacts, low-light noise, blur, and occlusions to enhance deployment robustness.A hybrid deep learning architecture was developed that combines CNN backbone networks with Vision Transformer (ViT) layers, multi-scale feature pyramid modules, and attention-based fusion blocks to capture both global semantics and subtle generative artifacts. The model was trained with stratified sampling, transfer learning, and controlled augmentation strategies. Comprehensive evaluation using accuracy, precision–recall, F1-score, ROC-AUC, and cross-generator testing demonstrates that the framework provides strong generalization to unseen generative models, including diffusion-based datasets. Results show significant improvements in robustness against real-world distortions and variability, enabling reliable application in digital forensics, content authentication, and AI-generated media regulation. The proposed system provides a promising pathway toward universal detectors capable of adapting to rapidly evolving generative technologies. Keywords: - Deepfake Detection, Generative Models, Diffusion Networks, Vision Transformers, Multi-Scale Features, Synthetic Image Forensics, Cross-Generator Generalization