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Trustworthy Image Authentication Using Forensic Knowledge Graphs
Book chapter

Trustworthy Image Authentication Using Forensic Knowledge Graphs

Tai D. Nguyen and Matthew C. Stamm
Computer Vision – ECCV 2026, pp 38-58
11 Sep 2026
DOI: https://doi.org/10.1007/978-3-032-37020-4_3
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.48550/arxiv.2606.23917View
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Abstract

Image Forensics Knowledge Graphs Trustworthy AI
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.

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