  With various facial manipulation techniques arising, face forgery detection
has drawn growing attention due to security concerns. Previous works always
formulate face forgery detection as a classification problem based on
cross-entropy loss, which emphasizes category-level differences rather than the
essential discrepancies between real and fake faces, limiting model
generalization in unseen domains. To address this issue, we propose a novel
face forgery detection framework, named Dual Contrastive Learning (DCL), which
specially constructs positive and negative paired data and performs designed
contrastive learning at different granularities to learn generalized feature
representation. Concretely, combined with the hard sample selection strategy,
Inter-Instance Contrastive Learning (Inter-ICL) is first proposed to promote
task-related discriminative features learning by especially constructing
instance pairs. Moreover, to further explore the essential discrepancies,
Intra-Instance Contrastive Learning (Intra-ICL) is introduced to focus on the
local content inconsistencies prevalent in the forged faces by constructing
local-region pairs inside instances. Extensive experiments and visualizations
on several datasets demonstrate the generalization of our method against the
state-of-the-art competitors.
