Trustworthy Image Authentication using Forensic Knowledge Graphs

Researchers propose Forensic Knowledge Graphs (FKGs) to unify forensic evidence extraction, structured reasoning, and interpretable explanations for image authentication. The framework outperforms existing forensic detectors and vision-language models in detection, forgery identification, localization, and justification. A new dataset FKG-50K with 50,000 realistic forgeries is introduced to support further research.

arXiv:2606.23917v1 Announce Type: new 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.