Face Forgery Detection Based on the Improved Siamese Network

Abstract

Face tampering is an intriguing task in video/image genuineness identification and has attracted significant amounts of attention in recent years. In this work, we propose a face forgery detection method that consists of preprocessing, an improved Siamese network-based feature extractor (including a feature alignment module), and postprocessing (a voting principle). Roughly speaking, our method extracts the features in the grey space of face/background image pairs and measures the difference to make decisions. Experiments on several standard databases prove the effectiveness of our method, and especially on the low-quality subdataset of the FaceForensics++ , our method achieves a competitive result.