{"ID":2847734,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00181","arxiv_id":"2511.00181","title":"From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection","abstract":"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.","short_abstract":"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 in...","url_abs":"https://arxiv.org/abs/2511.00181","url_pdf":"https://arxiv.org/pdf/2511.00181v2","authors":"[\"Mengfei Liang\",\"Yiting Qu\",\"Yukun Jiang\",\"Michael Backes\",\"Yang Zhang\"]","published":"2025-10-31T18:36:49Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CR\"]","methods":"[\"Language Model\"]","has_code":false}