Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector

Authors: Xiao Guo, Xiufeng Song, Yue Zhang, Xiaohong Liu, Xiaoming Liu

Published: 2025-03-26 03:28:46+00:00

AI Summary

This paper introduces M2F2-Det, a multi-modal face forgery detector that simultaneously outputs deepfake detection scores and textual explanations. It leverages pre-trained CLIP and LLMs to improve generalization and interpretability in deepfake detection, achieving state-of-the-art performance.

Abstract

Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separately, we introduce a novel method capable of generating both simultaneously. Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models (LLMs) to enhance both the generalization and explainability of deepfake detection. Specifically, we introduce a multi-modal face forgery detector (M2F2-Det) that employs tailored face forgery prompt learning, incorporating the pre-trained CLIP to improve generalization to unseen forgeries. Also, M2F2-Det incorporates an LLM to provide detailed textual explanations of its detection decisions, enhancing interpretability by bridging the gap between natural language and subtle cues of facial forgeries. Empirically, we evaluate M2F2-Det on both detection and explanation generation tasks, where it achieves state-of-the-art performance, demonstrating its effectiveness in identifying and explaining diverse forgeries.