Face Morphing Attack Detection Using Similarity Score Patterns Between De-Morphed and Live Images

Face morphing attacks have become a serious threat to Face Recognition Systems (FRSs). A de-morphing-based morphing attack detection method has been proposed and studied, which uses suspect and live capture, but the unknown morphing parameters in the used morphing algorithm make applying de-morphing methods challenging. This paper proposes a robust face morphing attack detection (FMAD) method (pipeline) leveraging deep learning de-morphing networks. Inspired by differences in similarity score (i.e., cosine similarity between feature vectors) variations between morphed and non-morphed images, the detection pipeline was proposed to learn the variation patterns of similarity scores between live capture and de-morphed face/bona fide images with different de-morphing factors. An effective deep de-morphing network based on StyleGAN and the pSp (pixel2style2pixel) encoder was developed. The network generates de-morphed images from suspect and live images with multiple de-morphing factors and calculates similarity scores between feature vectors from the ArcFace network, which are then classified by the detection network. Experiments on morphing datasets from the Color FERET, FRGCv2, and SYS-MAD databases, including landmark-based and deep learning attacks, demonstrate that the proposed method performs high accuracy in detecting unseen morphing attacks across different databases. It attains an Equal Error Rate (EER) of less than 1–4% and a Bona Fide Presentation Classification Error Rate (BPCER) of approximately 11% at an Attack Presentation Classification Error Rate (APCER) of 0.1%, outperforming previous methods.