# MLLM-Enhanced Face Forgery Detection: A Vision-Language Fusion Solution

arXiv: https://arxiv.org/abs/2505.02013 · HTML: https://mlsanity.com/paper/2505.02013/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2025.

## Built on by (most cited first, 15 of 15)

- [AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models](https://mlsanity.com/paper/2507.02664/) (2025)
- [BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM](https://mlsanity.com/paper/2507.14632/) (2025)
- [Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning](https://mlsanity.com/paper/2508.21048/) (2025)
- [Seeing Before Reasoning: A Unified Framework for Generalizable and Explainable Fake Image Detection](https://mlsanity.com/paper/2509.25502/) (2025)
- [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026)
- [On the Holistic Approach for Detecting Human Image Forgery](https://mlsanity.com/paper/2601.04715/) (2026)
- [AlignGemini: Generalizable AI-Generated Image Detection Through Task-Model Alignment](https://mlsanity.com/paper/2512.06746/) (2025)
- [Explainable Deepfake Detection with RL Enhanced Self-Blended Images](https://mlsanity.com/paper/2601.15624/) (2026)
- [VIGIL: Part-Grounded Structured Reasoning for Generalizable Deepfake Detection](https://mlsanity.com/paper/2603.21526/) (2026)
- [Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models](https://mlsanity.com/paper/2606.15880/) (2026)
- [VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection](https://mlsanity.com/paper/2604.13660/) (2026)
- [Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization](https://mlsanity.com/paper/2608.20913/) (2026)
- [Leveraging Unlabeled Data from Unknown Sources via Dual-Path Guidance for Deepfake Face Detection](https://mlsanity.com/paper/2508.09022/) (2025)
- [Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection](https://mlsanity.com/paper/2512.06363/) (2025)
- [MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection](https://mlsanity.com/paper/2608.17328/) (2026)

## Abstract (arXiv)

Reliable face forgery detection algorithms are crucial for countering the growing threat of deepfake-driven disinformation. Previous research has demonstrated the potential of Multimodal Large Language Models (MLLMs) in identifying manipulated faces. However, existing methods typically depend on either the Large Language Model (LLM) alone or an external detector to generate classification results, which often leads to sub-optimal integration of visual and textual modalities. In this paper, we propose VLF-FFD, a novel Vision-Language Fusion solution for MLLM-enhanced Face Forgery Detection. Our key contributions are twofold. First, we present EFF++, a frame-level, explainability-driven extension of the widely used FaceForensics++ (FF++) dataset. In EFF++, each manipulated video frame is paired with a textual annotation that describes both the forgery artifacts and the specific manipulation technique applied, enabling more effective and informative MLLM training. Second, we design a Vision-Language Fusion Network (VLF-Net) that promotes bidirectional interaction between visual and textual features, supported by a three-stage training pipeline to fully leverage its potential. VLF-FFD achieves state-of-the-art (SOTA) performance in both cross-dataset and intra-dataset evaluations, underscoring its exceptional effectiveness in face forgery detection.