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.

通过引入细粒度提示和粗细联合训练框架，提出了一种名为视觉语言人脸伪造检测（VLFFD）的新范式，以解决针对深度伪造的检测模型缺乏语义信息和解释性的问题，并在多个具有挑战性的基准测试中证明了该方法对现有的检测模型有所改进。

面向通用视觉语言人脸伪造检测

Towards General Visual-Linguistic Face Forgery Detection

In this paper, we propose a framework capable of generating face images that
fall into the same distribution as that of a given one-shot example. We
leverage a pre-trained StyleGAN model that already learned the generic face
distribution. Given the one-shot target, we develop an iterative optimization
scheme that rapidly adapts the weights of the model to shift the output's
high-level distribution to the target's. To generate images of the same
distribution, we introduce a style-mixing technique that transfers the
low-level statistics from the target to faces randomly generated with the
model. With that, we are able to generate an unlimited number of faces that
inherit from the distribution of both generic human faces and the one-shot
example. The newly generated faces can serve as augmented training data for
other downstream tasks. Such setting is appealing as it requires labeling very
few, or even one example, in the target domain, which is often the case of
real-world face manipulations that result from a variety of unknown and unique
distributions, each with extremely low prevalence. We show the effectiveness of
our one-shot approach for detecting face manipulations and compare it with
other few-shot domain adaptation methods qualitatively and quantitatively.

本文提出了一种框架，通过迭代优化和样式混合技术，生成与给定 one-shot 示例相同分布的人脸图像，可用于增强下游任务的训练数据，并在检测人脸操作方面表现出较高有效性，与其他少样本领域适应方法相比具有优越性。