Don't Guess, Escalate: Towards Explainable Uncertainty-Calibrated AI Forensic Agents
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Abstract (arXiv)
AI is reshaping the landscape of multimedia forensics. We propose AI forensic agents: reliable orchestrators that select and combine forensic detectors, identify provenance and context, and provide uncertainty-aware assessments. We highlight pitfalls in current solutions and introduce a unified framework to improve the authenticity verification process.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 18 Dec 2025.
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References · 9
- 2022Scalable Diffusion Models with Transformerscited by 5,931
- 2020Uncertainty Quantification and Deep Ensemblescited by 138
- 2024SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Modelcited by 107
- 2024FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Modelscited by 36
- 2025Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systemscited by 27
- 2024A Survey on Speech Deepfake Detectioncited by 26
- 2025HedgeAgents: A Balanced-aware Multi-agent Financial Trading Systemcited by 24
- 2024Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yetcited by 13
- 2025Survey on AI-Generated Media Detection: From Non-MLLM to MLLMcited by 12
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