Domain-Invariant Feature Learning for General Face Forgery Detection

Though existing methods for face forgery detection achieve fairly good performance under the intra-dataset scenario, few of them gain satisfying results in the case of cross-dataset testing with more practical value. To tackle this issue, in this paper, we propose a novel domain-invariant feature learning framework - DIFL for face forgery detection. In the framework, an adversarial domain generalization is introduced to learn the domain-invariant features from the forged samples synthesized by various algorithms. Then a center loss in fractional form (CL) is utilized to learn more discriminative features by aggregating the real faces while separating the fake faces from the real ones in the embedding space. In addition, a global and local random crop augmentation strategy is utilized to generate more data views of forged facial images at various scales. Extensive experimental results demonstrate the effectiveness and generalization of the proposed method compared with other state-of-the-art methods.