FACETRACER: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention

Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious users behind fraudulent activities. Intrusive watermark-based approaches also fail to trace unmarked identities, limiting their practical utility. To address these challenges, we introduce FACETRACER, the first non-intrusive framework specifically designed to trace the identity of the source person from swapped face images or videos. Specifically, FACETRACER leverages a disentanglement module that effectively suppresses identity information related to the target person while isolating the identity features of the source person. This allows us to extract robust identity information that can directly link the swapped face back to the original individual, aiding in uncovering the actors behind fraudulent activities. Extensive experiments demonstrate FACETRACER's effectiveness across various face-swapping techniques, successfully identifying the source person in swapped content and enabling the tracing of malicious actors involved in fraudulent activities. Additionally, FACETRACER shows strong transferability to unseen face-swapping methods including commercial applications and robustness against transmission distortions and adaptive attacks.