---
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}
}
```