IDRetracor: Towards Visual Forensics against Malicious Face Swapping

The deepfake-based face swapping technique poses significant risks to personal identity security. Although many detection methods have been proposed to counter malicious face swapping, they typically provide only binary labels (Fake/Real), lacking reliable and interpretable evidence. To address this limitation, we introduce a novel task called face retracing, which aims to visually trace back the original target face from a given fake one through inverse mapping. This task is based on the observation that current face swapping methods are neither flawless nor entirely random, leaving recoverable traces of the original identity. To this end, we propose IDRetracor, a model designed to recover arbitrary original target identities from fake faces generated by various face swapping techniques. Specifically, we first employ a mapping resolver to estimate the possible solution space of the original face for inverse mapping. Then, we introduce Mapping-Aware Convolutions (MACs), which consist of multiple dynamically combined kernels guided by the mapping resolver to adaptively handle diverse face swapping patterns. Extensive experiments demonstrate that IDRetracor achieves strong performance in retracing original faces, validated by both quantitative metrics and qualitative assessments.