Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
This work addresses face forgery detection, a critical issue for security and media integrity, but it is incremental as it builds on existing methods to improve feature learning.
The paper tackles the problem of insufficient discriminative features and fixed frequency filters in face forgery detection by proposing a single-center loss and an adaptive frequency feature generation module, achieving state-of-the-art results on the FF++ dataset.
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.