  Residual-domain feature is very useful for Deepfake detection because it
suppresses irrelevant content features and preserves key manipulation traces.
However, inappropriate residual prediction will bring side effects on detection
accuracy. In addition, residual-domain features are easily affected by image
operations such as compression. Most existing works exploit either
spatial-domain features or residual-domain features, while neglecting that two
types of features are mutually correlated. In this paper, we propose a guided
residuals network, namely GRnet, which fuses spatial-domain and residual-domain
features in a mutually reinforcing way, to expose face images generated by
Deepfake. Different from existing prediction based residual extraction methods,
we propose a manipulation trace extractor (MTE) to directly remove the content
features and preserve manipulation traces. MTE is a fine-grained method that
can avoid the potential bias caused by inappropriate prediction. Moreover, an
attention fusion mechanism (AFM) is designed to selectively emphasize feature
channel maps and adaptively allocate the weights for two streams. The
experimental results show that the proposed GRnet achieves better performances
than the state-of-the-art works on four public fake face datasets including
HFF, FaceForensics++, DFDC and Celeb-DF. Especially, GRnet achieves an average
accuracy of 97.72% on the HFF dataset, which is at least 5.25% higher than the
existing works.
