End-to-End Reconstruction-Classification Learning for Face Forgery Detection

Existing face forgery detectors mainly focus on specific forgery patterns like noise characteristics, local textures, or frequency statistics for forgery detection. This causes specialization of learned representations to known forgery patterns presented in the training set, and makes it difficult to detect forgeries with unknown patterns. In this paper, from a new perspective, we propose a forgery detection framework emphasizing the common compact representations of genuine faces based on reconstruction-classification learning. Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries. To achieve better representations, instead of only using the encoder in reconstruction learning, we build bipartite graphs over the encoder and decoder features in a multi-scale fashion. We further exploit the reconstruction difference as guidance of forgery traces on the graph output as the final representation, which is fed into the classifier for forgery detection. The reconstruction and classification learning is optimized end-to-end. Extensive experiments on large-scale benchmark datasets demonstrate the superiority of the proposed method over state of the arts.