[2606.23917] Trustworthy Image Authentication using Forensic Knowledge Graphs
[Submitted on 22 Jun 2026]
Title:Trustworthy Image Authentication using Forensic Knowledge Graphs
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Abstract:Advances in generative AI have made image falsification highly realistic, demanding trustworthy authentication systems. Existing forensic detectors can target certain forgery types but lack interpretability, while vision-language models (VLMs) provide explanations but cannot exploit forensic traces for reliable detection. We propose Forensic Knowledge Graphs (FKGs), a unified framework that integrates forensic evidence extraction, structured reasoning, and human-interpretable explanation. Our FKG structure encodes forensic traces along with their causal dependencies and links to scene content. To generate accurate FKGs, we introduce a novel forensic authentication network and an Iterative Context Refinement strategy that guides VLMs to produce faithful, grounded explanations. We also present FKG-50K, a dataset of 50,000 realistic forgeries with ground-truth FKGs. Experiments demonstrate that FKG outperforms both forensic detectors and VLMs in detection, forgery identification and localization, and forensic justification.
Comments: Accepted and Published at ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.23917 [cs.CV]
(or arXiv:2606.23917v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.23917
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tai Nguyen [view email] [v1] Mon, 22 Jun 2026 20:29:21 UTC (6,952 KB)
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