UniShield：基于知识图谱的多模态推理统一面部攻击检测
UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning
AI总结：
UniShield通过构建面部攻击知识图谱，结合图谱一致推理优化，提升面部攻击检测的准确性和推理可靠性。
摘要：
统一面部攻击检测（UAD）需要在共享决策空间中识别物理欺骗和数字伪造，但现有判别或提示方法主要依赖外观相关性，提供有限的证据支持推理。我们提出UniShield，一种基于知识的多模态推理框架，用于统一面部攻击防御。UniShield构建了一个面部攻击知识图谱（FAKG），将攻击类别链接到诊断视觉线索和攻击条件关系，并利用它合成52,025个FAKG-QA示例用于攻击图指令微调（AGIT）。为提高推理一致性，我们进一步引入图一致推理优化（GCRO），一种基于GRPO的目标，具有KG一致性奖励，鼓励生成的推理匹配图支持的线索，同时惩罚不兼容的声明。在我们的多模态UAD基准测试中，UniShield在二元、粗粒度和细粒度协议中均表现出强劲性能，具有持续高ACC和低HTER。这些结果表明，结构化的攻击知识可以提高检测准确性和推理可靠性，优于判别基线和通用多模态大语言模型。我们的代码将在https://anonymous.4open.science/r/Unishield-A6A3/上发布。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
