From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing

• URL: http://arxiv.org/abs/2603.01038v1
• Date: Sun, 01 Mar 2026 10:28:35 GMT
• Title: From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
• Authors: Haoyuan Zhang, Keyao Wang, Guosheng Zhang, Haixiao Yue, Zhiwen Tan, Siran Peng, Tianshuo Zhang, Xiao Tan, Kunbin Chen, Wei He, Jingdong Wang, Ajian Liu, Xiangyu Zhu, Zhen Lei,
• Abstract summary: We propose the Tool-Augmented Reasoning FAS (TAR-FAS) framework, which reformulates the Face Anti-Spoofing task as a Chain-of-Thought with Visual Tools (CoT-VT) paradigm.<n>We show that TAR-FAS achieves SOTA performance while providing fine-grained visual investigation for trustworthy spoof detection.
• Score: 47.26568822352609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classification task as the generation of brief textual descriptions to improve cross-domain generalization. However, their generalizability is still limited, as such descriptions mainly capture intuitive semantic cues (e.g., mask contours) while struggling to perceive fine-grained visual patterns. To address this limitation, we incorporate external visual tools into MLLMs to encourage deeper investigation of subtle spoof clues. Specifically, we propose the Tool-Augmented Reasoning FAS (TAR-FAS) framework, which reformulates the FAS task as a Chain-of-Thought with Visual Tools (CoT-VT) paradigm, allowing MLLMs to begin with intuitive observations and adaptively invoke external visual tools for fine-grained investigation. To this end, we design a tool-augmented data annotation pipeline and construct the ToolFAS-16K dataset, which contains multi-turn tool-use reasoning trajectories. Furthermore, we introduce a tool-aware FAS training pipeline, where Diverse-Tool Group Relative Policy Optimization (DT-GRPO) enables the model to autonomously learn efficient tool use. Extensive experiments under a challenging one-to-eleven cross-domain protocol demonstrate that TAR-FAS achieves SOTA performance while providing fine-grained visual investigation for trustworthy spoof detection.

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