| library_name: transformers | |
| pipeline_tag: image-text-to-text | |
| # VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection | |
| [**Paper**](https://huggingface.co/papers/2604.13660) | [**Official GitHub**](https://github.com/abigcatcat/VRAG-DFD) | |
| VRAG-DFD is a framework that introduces Verifiable Retrieval-Augmented Generation (RAG) into the Deepfake Detection (DFD) domain. By combining professional forensic knowledge retrieval with Reinforcement Learning (Group Relative Policy Optimization - GRPO), it empowers Multi-modal Large Language Models (MLLMs) to perform expert-level forensic analysis with critical reasoning. | |
| ## Overview | |
| In Deepfake Detection (DFD) tasks, existing MLLM-based methods often lack professional forgery knowledge. VRAG-DFD addresses this by: | |
| - **Accurate Dynamic Retrieval**: Providing high-quality associated forgery knowledge via a Forensic Knowledge Database (FKD). | |
| - **Critical Reasoning**: Using the Forensic Chain-of-Thought (F-CoT) dataset and RL to help the model distinguish between visual evidence and potentially noisy retrieval information. | |
| - **Three-Stage Training**: A progressive pipeline consisting of Visual Alignment, Forensic SFT, and Critical RL (GRPO). | |
| The model is based on the Qwen2.5-VL architecture and achieves state-of-the-art performance on DFD generalization testing. | |
| ## Citation | |
| If you use our dataset, code or find VRAG-DFD useful, please cite our paper: | |
| ```bibtex | |
| @article{vragdfd2025, | |
| title={VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection}, | |
| author={Hui Han and Shunli Wang and Yandan Zhao and Taiping Yao and Shouhong Ding}, | |
| journal={arXiv preprint}, | |
| year={2026}, | |
| note={Available soon} | |
| } | |
| ``` |