用知识图谱融合伪造痕迹与推理，实现可解释的可信图像认证
Trustworthy Image Authentication using Forensic Knowledge Graphs
- 构建包含伪造痕迹与因果关系的结构化知识图谱
- 在5万张伪造图像上验证，检测与定位精度显著优于现有方法
- 适合需要可解释性安全认证的场景，如司法取证
生成式AI的发展使图像伪造愈发逼真，亟需可信的认证系统。现有取证检测器针对特定伪造类型，但缺乏可解释性；视觉语言模型（VLMs）能提供解释，却无法有效利用取证痕迹进行可靠检测。本文提出取证知识图谱（Forensic Knowledge Graphs, FKG），一个统一框架，集成取证证据提取、结构化推理与人类可读解释。FKG结构编码伪造痕迹及其因果依赖，并关联场景内容。为生成准确的FKG，我们设计新型取证认证网络与迭代上下文优化策略，引导VLM生成真实、有依据的解释。此外，我们构建了包含5万张真实伪造图像的基准数据集FKG-50K，附带真实标注的FKG。实验表明，FKG在检测、伪造识别与定位、以及取证论证方面均优于传统检测器与VLM。
原文摘要 · Abstract (English)
Advances in generative AI have made image falsification highly realistic, demanding trustworthy authentication systems. Existing forensic detectors can target certain forgery types but lack interpretability, while vision-language models (VLMs) provide explanations but cannot exploit forensic traces for reliable detection. We propose Forensic Knowledge Graphs (FKGs), a unified framework that integrates forensic evidence extraction, structured reasoning, and human-interpretable explanation. Our FKG structure encodes forensic traces along with their causal dependencies and links to scene content. To generate accurate FKGs, we introduce a novel forensic authentication network and an Iterative Context Refinement strategy that guides VLMs to produce faithful, grounded explanations. We also present FKG-50K, a dataset of 50,000 realistic forgeries with ground-truth FKGs. Experiments demonstrate that FKG outperforms both forensic detectors and VLMs in detection, forgery identification and localization, and forensic justification.
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