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

Abstract

The proliferation of hyper-realistic AI-generated images poses significant threats to digital information integrity and forensic accountability. Existing detection methodologies, however, face three critical bottlenecks: vulnerability to real-world distortions such as social media compression, inadequate specialization for socially harmful “ex-regulatory” content, and an inability to perform model attribution essential for effective governance. To address these challenges, we propose the Multi-level Feature Fusion Detection and Attribution Framework (MF2DA), a unified end-to-end pipeline designed for both high-precision detection and reliable model attribution. The core architecture synergizes an Edge Pyramid Fusion ResNet (EPF-ResNet), which captures subtle pixel-level edge artifacts, with a frozen CLIP-ViT to ensure robust semantic generalization. Furthermore, the framework is augmented by the MLLM-Guided Quality Refinement Module (MQRM), which adaptively leverages semantic-agnostic quality features to decouple generative traces from aggressive compression noise. Finally, the Dual-stream Differential Patch Attribution Network (D2PAN) extracts resilient model fingerprints by disentangling micro-textural patterns from semantic interference, thereby achieving precise generator identification. Extensive evaluations on multiple benchmarks demonstrate that MF2DA achieves state-of-the-art performance in detecting both “friendly” and “ex-regulatory” images while maintaining exceptional cross-dataset generalization. By integrating robust detection with precise attribution, this work establishes a practical and accountable forensic solution for the rapidly evolving generative AI landscape.