Detecting AI-Generated Images Using Vision Transformers (ViT)

In this video, I build and evaluate a Vision Transformer (ViT) model to classify real vs AI-generated images using the CIFAKE dataset, which contains both authentic CIFAR-10 images and synthetic images generated by Stable Diffusion. I walk through the full pipeline, including dataset preparation, model architecture, training process, and evaluation results, and analyze how well ViT can detect subtle patterns that distinguish real images from fake ones. This project demonstrates the power of transformer-based models in computer vision and highlights their potential for AI-generated image detection, making it useful for students, researchers, and anyone interested in modern deep learning approaches. Kaggle Code : https://www.kaggle.com/code/amineipad/vitfakevsreal