HDL: Hybrid and Dynamic Learning for Fake Face Recognition

Abstract

Face swapping aims to replace a source face with a target face, generating a fake face that is indistinguishable from the real one to the human eye. Existing face recognition methods usually discriminate the fake face as the target face identity, which happens to be misguided. To address this embarrassment, we pioneer a new task called “fake face recognition,” which seeks to discover the identity of the source face based on the fake face. Besides, we design a hybrid and dynamic learning strategy for fake face recognition. Specifically, we hybridize the existing real face recognition dataset with the fake face dataset. Based on the popular margin-based face recognition approach, we achieve dynamic learning by adjusting the margin for the fake face samples. The deep network is guided to first focus on real samples and then explores the identity of implicit commonalities between real and fake samples. To verify the performance of the fake face recognition model, we further organize the existing fake face datasets into face pairs. Extensive experiments on the fake face datasets show that our proposed hybrid and dynamic learning strategy achieves superior average accuracy (98.46%) compared to benchmark studies.