DLFMNet: End-to-End Detection and Localization of Face Manipulation Using Multi-Domain Features

Recently, more and more realistic facial manipulation images and videos, known as DeepFakes, have been created and rapidly circulated in social media. Therefore, it is crucial to develop effective and efficient methods to detect the malicious DeepFakes. Previous approaches all adopt a two-step pipeline with multiple separate models, i.e., first face detection and then face forensics, and lacks robustness against compressed data. In this paper, we propose an end-to-end framework for detection and localization of face manipulation, named DLFMNet, which effectively integrates face detection and face forensics into one model, avoiding intermediate processes like image cropping and feature re-extraction. In addition, to capture richer and more robust manipulated clues, we exploit multi-domain features that takes advantages of two different but complementary domains (i.e., RGB and noise). The evaluations on FaceForensics++ dataset demonstrate the effectiveness of our proposed DLFMNet. https://github.com/LightningChan/DLFMNet.