Towards Explainable Fake Image Detection with Multi-Modal Large Language Models

摘要

Recent advancements in image generation have provoked social and security concerns, yet most detection methods rely on black-box models that generalize poorly. By utilizing advances in Multi-modal Large Language Models (MLLMs), we propose a framework that fuses six specialized paradigms, each analyzing a distinct aspect of the image, to provide a final verdict with coherent, evidence-based reasoning. Experimental results on a diverse dataset of real and AI-generated images demonstrate that our approach outperforms both traditional detection methods and top humans, while providing . This study underscores the potential of MLLMs in developing robust, explainable, and reasoning-driven detection systems. The code is available at https://github.com/Gennadiyev/mllm-defake.