  With the rapid development of facial forgery techniques, forgery detection
has attracted more and more attention due to security concerns. Existing
approaches attempt to use frequency information to mine subtle artifacts under
high-quality forged faces. However, the exploitation of frequency information
is coarse-grained, and more importantly, their vanilla learning process
struggles to extract fine-grained forgery traces. To address this issue, we
propose a progressive enhancement learning framework to exploit both the RGB
and fine-grained frequency clues. Specifically, we perform a fine-grained
decomposition of RGB images to completely decouple the real and fake traces in
the frequency space. Subsequently, we propose a progressive enhancement
learning framework based on a two-branch network, combined with
self-enhancement and mutual-enhancement modules. The self-enhancement module
captures the traces in different input spaces based on spatial noise
enhancement and channel attention. The Mutual-enhancement module concurrently
enhances RGB and frequency features by communicating in the shared spatial
dimension. The progressive enhancement process facilitates the learning of
discriminative features with fine-grained face forgery clues. Extensive
experiments on several datasets show that our method outperforms the
state-of-the-art face forgery detection methods.
