EviTrace: Structured Evidence-Guided Progressive Reasoning for Interpretable Face Anti-Spoofing

Existing face anti-spoofing methods typically apply a uniform one-pass pipeline to all samples, despite the substantial variation in the saliency and diagnostic difficulty of spoofing cues. While some attacks can be identified from obvious artifacts with minimal reasoning, others exhibit only subtle and hard-to-perceive clues that require more careful evidence aggregation. To address this limitation, we propose EviTrace, a structured Evidence-guided progressive reasoning framework for interpretable face anti-spoofing. Specifically, we first introduce Structured Evidence Slots (SES), which leverage structured priors during training to learn explicit responses to diverse spoof-related cues beyond sample-level binary live/spoof supervision. Building on these structured evidence responses, we further develop Difficulty-Aware Progressive Reasoning (DPR), which pools high-confidence slot evidence for coarse screening and selectively routes low-confidence samples for refinement by revisiting the most ambiguous slots and their most relevant local patch regions. Rather than replacing the coarse prediction, DPR progressively corrects it through residual evidence aggregation. Based on this evidence-to-decision process, EviTrace naturally yields an Interpretable Reasoning Trace (IRT) that explicitly reveals evidence activation, uncertainty estimation, selective refinement, and final correction as a transparent decision chain. Experiments under a generalization setting show that EviTrace achieves competitive performance while providing a clearer and more transparent reasoning process. Code is available at https://anonymous.4open.science/r/EviTrace