The remarkable success in face forgery techniques has received considerable
attention in computer vision due to security concerns. We observe that
up-sampling is a necessary step of most face forgery techniques, and cumulative
up-sampling will result in obvious changes in the frequency domain, especially
in the phase spectrum. According to the property of natural images, the phase
spectrum preserves abundant frequency components that provide extra information
and complement the loss of the amplitude spectrum. To this end, we present a
novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial
image and phase spectrum to capture the up-sampling artifacts of face forgery
to improve the transferability, for face forgery detection. And we also
theoretically analyze the validity of utilizing the phase spectrum. Moreover,
we notice that local texture information is more crucial than high-level
semantic information for the face forgery detection task. So we reduce the
receptive fields by shallowing the network to suppress high-level features and
focus on the local region. Extensive experiments show that SPSL can achieve the
state-of-the-art performance on cross-datasets evaluation as well as
multi-class classification and obtain comparable results on single dataset
evaluation.

本文提出了一种利用空间图像和相位谱相结合的 SPSL 方法，以捕捉人脸伪造的上采样伪影并提高可转移性，通过减少感受野来抑制高级特征，专注于本地区域的人脸伪造检测方法，并对利用相位谱的有效性进行了理论分析，在交叉数据集评估中实现了最先进的性能，并在单个数据集评估中获得可比较的结果。