  Deepfakes are realistic face manipulations that can pose serious threats to
security, privacy, and trust. Existing methods mostly treat this task as binary
classification, which uses digital labels or mask signals to train the
detection model. We argue that such supervisions lack semantic information and
interpretability. To address this issues, in this paper, we propose a novel
paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses
fine-grained sentence-level prompts as the annotation. Since text annotations
are not available in current deepfakes datasets, VLFFD first generates the
mixed forgery image with corresponding fine-grained prompts via Prompt Forgery
Image Generator (PFIG). Then, the fine-grained mixed data and coarse-grained
original data and is jointly trained with the Coarse-and-Fine Co-training
framework (C2F), enabling the model to gain more generalization and
interpretability. The experiments show the proposed method improves the
existing detection models on several challenging benchmarks. Furthermore, we
have integrated our method with multimodal large models, achieving noteworthy
results that demonstrate the potential of our approach. This integration not
only enhances the performance of our VLFFD paradigm but also underscores the
versatility and adaptability of our method when combined with advanced
multimodal technologies, highlighting its potential in tackling the evolving
challenges of deepfake detection.
