Forensic Symmetry for DeepFakes

In this paper, we propose a new DeepFakes forensics approach called forensic symmetry, which determines whether two symmetrical face patches contain the same or different natural features. To do this, we propose a multi-stream learning structure composed of two feature extractors. The first feature extractor obtains symmetry feature from the front face images. The second feature extractor obtains similarity feature from the side face images. Symmetry feature and similarity feature are collectively called natural feature. Forensic symmetry system maps the pair of symmetrical face patches into the angular hyperspace to quantify the difference of their natural features. The greater the difference of natural features, the higher the tamper probability of face images. The heuristic prediction algorithm is designed to compute the tamper probability of DeepFakes at video level. A series of experiments are carried out to evaluate the effectiveness of our proposed forensic symmetry system. Experimental results show that our approach is effective for DeepFakes detection under the scenarios of homologous detection, heterogeneous detection, and re- compression detection.