Dissecting Agentic Forensics: The Role of Triage, Prompting, and Evidence Arbitration in Open-World Fake Image Detection
Agentic fake-image forensics depends more on the MLLM judge's reasoning than on detector fusion.
A cs.CR paper evaluates a training-free agentic image-forensics framework that combines specialist detectors, per-detector triage, and conflict-aware evidence arbitration. Across six configurations and three multimodal LLM backbones, naive detector fusion produced severe false positives on authentic images, while triage and prompting improved calibration. Reasoning quality dominated: a stronger judge beat a weaker one, especially out of distribution. Manipulation recall was nearly saturated, so the main open-world challenge is trusting and arbitrating forensic evidence rather than finding edits.
- Naive fusion of specialist detectors produces severe false positives on authentic images.
- Triage and prompting filter unreliable evidence and expose detector limitations.
- A stronger multimodal judge outperforms a weaker one, especially under distribution shift.
- Manipulation recall is nearly saturated; the hard problem is calibrating trust.
From the original article
1 min read at arxiv.orgImage 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.
By Xianlong Li · arxiv.org
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