  Previous deepfake detection methods mostly depend on low-level textural
features vulnerable to perturbations and fall short of detecting unseen forgery
methods. In contrast, high-level semantic features are less susceptible to
perturbations and not limited to forgery-specific artifacts, thus having
stronger generalization. Motivated by this, we propose a detection method that
utilizes high-level semantic features of faces to identify inconsistencies in
temporal domain. We introduce UniForensics, a novel deepfake detection
framework that leverages a transformer-based video classification network,
initialized with a meta-functional face encoder for enriched facial
representation. In this way, we can take advantage of both the powerful
spatio-temporal model and the high-level semantic information of faces.
Furthermore, to leverage easily accessible real face data and guide the model
in focusing on spatio-temporal features, we design a Dynamic Video
Self-Blending (DVSB) method to efficiently generate training samples with
diverse spatio-temporal forgery traces using real facial videos. Based on this,
we advance our framework with a two-stage training approach: The first stage
employs a novel self-supervised contrastive learning, where we encourage the
network to focus on forgery traces by impelling videos generated by the same
forgery process to have similar representations. On the basis of the
representation learned in the first stage, the second stage involves
fine-tuning on face forgery detection dataset to build a deepfake detector.
Extensive experiments validates that UniForensics outperforms existing face
forgery methods in generalization ability and robustness. In particular, our
method achieves 95.3\% and 77.2\% cross dataset AUC on the challenging
Celeb-DFv2 and DFDC respectively.
