  In this paper, we propose a novel image forgery detection paradigm for
boosting the model learning capacity on both forgery-sensitive and genuine
compact visual patterns. Compared to the existing methods that only focus on
the discrepant-specific patterns (\eg, noises, textures, and frequencies), our
method has a greater generalization. Specifically, we first propose a
Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual
patterns. DisGE consists of two branches, where the mainstream backbone branch
is used to extract general semantic features, and the accessorial discrepant
external attention branch is used to extract explicit forgery cues. Besides, a
Double-Head Reconstruction (DouHR) module is proposed to enhance genuine
compact visual patterns in different granular spaces. Under DouHR, we further
introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine
compact visual patterns, such that the forgery detection capability on unknown
patterns can be improved. Extensive experimental results on four challenging
datasets validate the effectiveness of our proposed method against
state-of-the-art competitors.
