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.

翻译：深度伪造是逼真的人脸篡改技术，可能对安全、隐私和信任构成严重威胁。现有方法大多将此任务视为二分类问题，使用数字标签或掩码信号训练检测模型。我们认为这类监督缺乏语义信息和可解释性。为解决这一问题，本文提出一种名为视觉语言人脸伪造检测（VLFFD）的新范式，采用细粒度句子级提示作为标注。由于当前深度伪造数据集缺乏文本标注，VLFFD首先通过提示伪造图像生成器（PFIG）生成带有对应细粒度提示的混合伪造图像。随后，利用粗细粒度联合训练框架（C2F）对细粒度混合数据与粗粒度原始数据进行联合训练，使模型获得更强的泛化能力和可解释性。实验表明，所提方法在多个具有挑战性的基准测试上提升了现有检测模型的性能。