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arXiv · 2605.08709

UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning

Hongrui Li·Yichen Shi·Hongyang Wang·Yuhao Gao·Hui Ma·Jun Feng·Zitong Yu
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Abstract

Unified face attack detection (UAD) requires recognizing physical spoofing and digital forgery within a shared decision space, yet existing discriminative or prompt-based methods largely rely on appearance correlations and provide limited evidence-grounded reasoning. We propose UniShield, a knowledge-grounded multimodal reasoning framework for unified face attack defense. UniShield constructs a Face Attack Knowledge Graph (FAKG) that links attack categories to diagnostic visual cues and attack-conditioned relations, and uses it to synthesize 52,025 FAKG-QA examples for Attack-Graph Instruction Tuning (AGIT). To improve rationale consistency, we further introduce Graph-Consistent Reasoning Optimization (GCRO), a GRPO-based objective with a KG-consistency reward that encourages generated rationales to match graph-supported cues while penalizing incompatible claims. Experiments on our multimodal UAD benchmark show that UniShield achieves strong performance across binary, coarse-grained, and fine-grained protocols, with consistently high ACC and low HTER. These results suggest that structured attack knowledge can improve both detection accuracy and reasoning reliability over discriminative baselines and general-purpose MLLMs. Our code will be released at https://anonymous.4open.science/r/Unishield-A6A3/.

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Hongrui Li, Yichen Shi, Hongyang Wang, Yuhao Gao, Hui Ma, Jun Feng, Zitong Yu. 2026-05-09. UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning. https://arxiv.org/abs/2605.08709

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