Analyzing the effect of reasoning-based supervision on face anti-spoofing

Face anti-spoofing (FAS) has become a crucial component in securing face recognition systems against presentation attacks, such as printed photos, replay videos, and 3D masks. While recent advances have improved generalization to unseen spoofing attempts, many existing methods remain black-box models that provide binary decisions without interpretable reasoning. In this paper, we investigate explainable face anti-spoofing from a supervision-centric perspective, using a vision-language model (VLM) to analyze how natural language explanations influence model behavior. To enable this study under controlled conditions, we construct an explanation-augmented benchmark by enriching four standard FAS datasets—MSU-MFSD, CASIA-FASD, Replay-Attack, and OULU-NPU—with both vanilla and reasoning-structured captions generated via the GPT-4o API. We further adopt a dual-objective training strategy that combines spoof classification loss with explanation generation loss, allowing us to examine the effect of explanation-based supervision while keeping the backbone architecture fixed. Through extensive cross-dataset evaluations, we show that reasoning-style captions can enhance detection performance and domain generalization in many settings, while also introducing inductive biases that may degrade performance when emphasized cues are misaligned with unseen attack types. These findings suggest that explanations in FAS should be viewed not only as interpretable outputs, but also as controllable training signals that shape generalization behavior. To support reproducibility, we publicly release the explanation annotations and associated metadata—excluding all face images—via a Hugging Face repository at https://huggingface.co/datasets/DescriptiveFAS/MCIO_public.