[2603.01038] From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.01038 (cs) [Submitted on 1 Mar 2026] 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 View a PDF of the paper titled From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing, by Haoyuan Zhang and 13 other authors View PDF HTML (experimental) 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 fo...