  As the remarkable development of facial manipulation technologies is
accompanied by severe security concerns, face forgery detection has become a
recent research hotspot. Most existing detection methods train a binary
classifier under global supervision to judge real or fake. However, advanced
manipulations only perform small-scale tampering, posing challenges to
comprehensively capture subtle and local forgery artifacts, especially in high
compression settings and cross-dataset scenarios. To address such limitations,
we propose a novel framework named Multi-modal Contrastive Classification by
Locally Correlated Representations(MC-LCR), for effective face forgery
detection. Instead of specific appearance features, our MC-LCR aims to amplify
implicit local discrepancies between authentic and forged faces from both
spatial and frequency domains. Specifically, we design the shallow style
representation block that measures the pairwise correlation of shallow feature
maps, which encodes local style information to extract more discriminative
features in the spatial domain. Moreover, we make a key observation that subtle
forgery artifacts can be further exposed in the patch-wise phase and amplitude
spectrum and exhibit different clues. According to the complementarity of
amplitude and phase information, we develop a patch-wise amplitude and phase
dual attention module to capture locally correlated inconsistencies with each
other in the frequency domain. Besides the above two modules, we further
introduce the collaboration of supervised contrastive loss with cross-entropy
loss. It helps the network learn more discriminative and generalized
representations. Through extensive experiments and comprehensive studies, we
achieve state-of-the-art performance and demonstrate the robustness and
generalization of our method.
