Abstract
Image forensics is increasingly an open-world problem: manipulations range from fully synthetic images to localized edits, splicing and swapping, while most forensic detectors remain specialized to a single manipulation family. Agentic AI has recently emerged as a promising solution. In principle, such systems can assess the reliability of individual detectors, identify out-of-scope evidence, and arbitrate conflicting reports. However, it remains unclear which components actually drive performance and whether their benefits persist under distribution shift. To answer these questions, we study a training-free agentic framework built around specialist detectors, per-detector triage, and conflict-aware evidence arbitration. Using six configurations and three multimodal large language model backbones, we dissect the role of triage, prompting, and reasoning quality on both in-distribution and out-of-distribution data. Our results show that naive detector fusion suffers from severe false-positive rates on authentic images. Triage and prompting consistently improve performance by filtering unreliable evidence and exposing detector limitations. However, the dominant factor is represented by reasoning itself: A stronger judge substantially outperforms a weaker one, particularly under distribution shift. Most notably, manipulation recall is nearly saturated across all configurations, indicating that the main challenge of open-world image forensics is not detecting manipulations, but calibrating trust in specialized forensic tools and arbitrating conflicting evidence.
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Publication details
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- Green open access
Cite this article
APA 7
Li, X., Bongini, P., Pancino, N., Blanchini, M., Tondi, B., & Barni, M. (2026). Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection. https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection
MLA 9
Li, Xianlong, et al. "Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection." https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection.
Chicago (author–date)
Li, Xianlong, Pietro Bongini, Niccoló Pancino, Marco Blanchini, Benedetta Tondi, and Mauro Barni. 2026. "Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection." https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection.
Harvard
Li, X., Bongini, P., Pancino, N., Blanchini, M., Tondi, B. and Barni, M. (2026) 'Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection', Available at: https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection.
Vancouver
Li X, Bongini P, Pancino N, Blanchini M, Tondi B, Barni M. Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection. https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection
IEEE
X. Li, P. Bongini, N. Pancino, M. Blanchini, B. Tondi, and M. Barni, "Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection," https://omanscience.com/en/articles/dissecting-agentic-forensics-the-role-of-triage-prompting-and-evidence-arbitration-in-open-world-fake-image-detection.