  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.
