  Face forgery detection is raising ever-increasing interest in computer vision
since facial manipulation technologies cause serious worries. Though recent
works have reached sound achievements, there are still unignorable problems: a)
learned features supervised by softmax loss are separable but not
discriminative enough, since softmax loss does not explicitly encourage
intra-class compactness and interclass separability; and b) fixed filter banks
and hand-crafted features are insufficient to capture forgery patterns of
frequency from diverse inputs. To compensate for such limitations, a novel
frequency-aware discriminative feature learning framework is proposed in this
paper. Specifically, we design a novel single-center loss (SCL) that only
compresses intra-class variations of natural faces while boosting inter-class
differences in the embedding space. In such a case, the network can learn more
discriminative features with less optimization difficulty. Besides, an adaptive
frequency feature generation module is developed to mine frequency clues in a
completely data-driven fashion. With the above two modules, the whole framework
can learn more discriminative features in an end-to-end manner. Extensive
experiments demonstrate the effectiveness and superiority of our framework on
three versions of the FF++ dataset.
