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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

60500 - Other Humanities and the Arts

Projekt

<a href="/cs/project/VK01010153" target="_blank" >VK01010153: Vývoj umělé inteligence pro systém multimodální nedestruktivní forenzní analýzy materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Heritage Science

ISSN

2050-7445

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

001480426900001

EID výsledku v databázi Scopus

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