A deep learning approach for anomaly detection in X-ray images of paintings

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0197805" target="_blank" >RIV/00216305:26220/26:0197805 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s40494-025-01724-9" target="_blank" >https://www.nature.com/articles/s40494-025-01724-9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s40494-025-01724-9" target="_blank" >10.1038/s40494-025-01724-9</a>

Result language
angličtina
Original language name
A deep learning approach for anomaly detection in X-ray images of paintings
Original language description
The intersection of technological advancements and cultural heritage studies has intensified the exploration of historical treasures, captivating historians and enthusiasts alike. Artificial intelligence now plays a key role in forensic art investigations by uncovering hidden patterns to detect forgeries. This study focuses on anomaly detection in X-ray images of paintings using the Ghent Altarpiece for training and testing purposes. We propose a novel model combining a Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM), a Nested U-Net, and a new dataset derived from the Altarpiece. The proposed architecture was benchmarked against several state-of-the-art deep learning techniques in anomaly detection. Our model achieved an accuracy of 0.8399 and an F1 score of 0.7869, outperforming other methods in both accuracy and computational efficiency. Results, validated by a domain expert, show strong precision and computational efficiency through semi-supervised learning.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
60500 - Other Humanities and the Arts

Project
<a href="/en/project/VK01010153" target="_blank" >VK01010153: Development of artificial intelligence for multimodal non-destructive forensic material analysis system</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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