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

Summary

MF2DA is a unified end-to-end framework for detecting and attributing AI-generated images, aimed at robustness to real-world distortions such as social media compression. It combines an Edge Pyramid Fusion ResNet (EPF-ResNet) for pixel-level edge artifacts, a frozen CLIP-ViT for semantic generalization, an MLLM-Guided Quality Refinement Module (MQRM), and a Dual-stream Differential Patch Attribution Network (D2PAN) for generator fingerprints. The authors report state-of-the-art detection of both "friendly" and "ex-regulatory" images with strong cross-dataset generalization across multiple benchmarks.