The rapid evolution of AI-generated images poses growing challenges to information integrity and media authenticity. Existing detection approaches face limitations in robustness, interpretability, and generalization across diverse generative models, particularly when relying on a single source of visual evidence. We introduce AIFo (Agent-based Image Forensics), a training-free framework that formulates AI-generated image detection as a multi-stage forensic analysis process through multi-agent collaboration. The framework integrates a set of forensic tools, including reverse image search, metadata extraction, pre-trained classifiers, and vision-language model analysis, and resolves insufficient or conflicting evidence through a structured multi-agent debate mechanism. An optional memory-augmented module further enables the framework to incorporate information from historical cases. We evaluate AIFo on a benchmark of 6,000 images spanning controlled laboratory settings and challenging real-world scenarios, where it achieves 97.05% accuracy and consistently outperforms traditional classifiers and strong vision-language model baselines. These findings demonstrate the effectiveness of agent-based procedural reasoning for AI-generated image detection.

翻译：AI生成图像的快速发展对信息完整性和媒体真实性构成了日益严峻的挑战。现有检测方法在鲁棒性、可解释性以及跨不同生成模型的泛化能力方面存在局限，尤其是当依赖单一视觉证据来源时。本文提出AIFo（基于智能体的图像取证），一种无需训练的多智能体协作框架，将AI生成图像检测形式化为多阶段取证分析过程。该框架集成了反向图像搜索、元数据提取、预训练分类器与视觉语言模型分析等取证工具，并通过结构化的多智能体辩论机制解决证据不足或相互冲突的问题。可选的记忆增强模块进一步使框架能够从历史案例中整合信息。我们在涵盖受控实验室环境与挑战性真实场景的6000张图像基准数据集上评估了AIFo，其准确率达到97.05%，持续优于传统分类器与强大的视觉语言模型基线。这些结果证明了基于智能体的程序推理用于AI生成图像检测的有效性。