MF2DA: Multi-Level Feature Fusion for Robust Detection and Attribution of Universal AI-Generated Images

AI-generated images that look very realistic are becoming a major problem for information trustworthiness and accountability, and current detection methods struggle with three main issues: they fail when images are compressed on social media, they don't work well on harmful content, and they can't identify which AI model created the image. This paper proposes MF2DA, a system that combines multiple AI techniques (including edge detection for pixel-level artifacts and CLIP-ViT, a model trained to understand both images and text) to both detect AI-generated images and identify which generator created them, even after social media compression.