  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.
