Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in color space. While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces {\em FreqBlender}, a new method that can generate pseudo-fake faces by blending frequency knowledge. Specifically, we investigate the major frequency components and propose a Frequency Parsing Network to adaptively partition frequency components related to forgery traces. Then we blend this frequency knowledge from fake faces into real faces to generate pseudo-fake faces. Since there is no ground truth for frequency components, we describe a dedicated training strategy by leveraging the inner correlations among different frequency knowledge to instruct the learning process. Experimental results demonstrate the effectiveness of our method in enhancing DeepFake detection, making it a potential plug-and-play strategy for other methods.

翻译：生成合成伪人脸（即伪伪造人脸）是提升深度伪造检测泛化能力的有效方式。现有方法通常在颜色空间混合真实或伪造人脸来生成此类图像。尽管这些方法已显现潜力，但忽略了模拟伪伪造人脸中的频率分布，限制了通用伪造痕迹的深度习得。为此，本文提出一种通过混合频率知识生成伪伪造人脸的新方法——FreqBlender。具体而言，我们分析了主要频率分量，并提出频率解析网络自适应分割与伪造痕迹相关的频率分量。随后，我们将伪造人脸中的频率知识融入真实人脸以生成伪伪造人脸。针对频率分量缺乏真值标签的问题，我们设计了一种专用训练策略，利用不同频率知识间的内在关联指导学习过程。实验结果表明，该方法能有效增强深度伪造检测性能，并具备作为其他方法的即插即用策略的潜力。