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 frame-work 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…

• NN
  National Natural Science Foundation of China

  Award: 61906119,U19B2035