用知识图谱提升人脸攻击检测的准确性和推理可靠性
UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning
- 构建攻击知识图谱，关联攻击类型与视觉线索
- 生成5.2万组问答数据，提升多模态推理能力
- 适合需要可解释检测的安防与身份认证场景
统一人脸攻击检测（UAD）需在共享决策空间中识别物理伪造与数字篡改，现有判别或提示方法多依赖外观相关性，缺乏证据支撑的推理。我们提出UniShield，一种基于知识图谱的多模态推理框架。该框架构建人脸攻击知识图谱（FAKG），将攻击类别与诊断视觉线索、攻击条件关系相连接，并据此合成52,025个FAKG-QA样本用于攻击图指令微调（AGIT）。为提升推理一致性，进一步引入图一致性推理优化（GCRO），一种基于GRPO的目标函数，包含知识图谱一致性奖励，鼓励生成的推理匹配图谱支持的线索，同时惩罚不一致声明。在自建的多模态UAD基准上实验表明，UniShield在二分类、粗粒度与细粒度协议下均表现优异，准确率（ACC）高且误报率（HTER）低。结果表明，结构化攻击知识能显著提升检测准确率与推理可靠性，优于判别基线与通用多模态大模型。
原文摘要 · Abstract (English)
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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