  Current face forgery detection methods achieve high accuracy under the
within-database scenario where training and testing forgeries are synthesized
by the same algorithm. However, few of them gain satisfying performance under
the cross-database scenario where training and testing forgeries are
synthesized by different algorithms. In this paper, we find that current
CNN-based detectors tend to overfit to method-specific color textures and thus
fail to generalize. Observing that image noises remove color textures and
expose discrepancies between authentic and tampered regions, we propose to
utilize the high-frequency noises for face forgery detection. We carefully
devise three functional modules to take full advantage of the high-frequency
features. The first is the multi-scale high-frequency feature extraction module
that extracts high-frequency noises at multiple scales and composes a novel
modality. The second is the residual-guided spatial attention module that
guides the low-level RGB feature extractor to concentrate more on forgery
traces from a new perspective. The last is the cross-modality attention module
that leverages the correlation between the two complementary modalities to
promote feature learning for each other. Comprehensive evaluations on several
benchmark databases corroborate the superior generalization performance of our
proposed method.
