ForgAnoNet: A Neural Network for Anomaly Detection in Artworks Using X-ray and Visible Spectrum Imaging
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198745" target="_blank" >RIV/00216305:26220/26:0198745 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1296207425001888" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1296207425001888</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.culher.2025.08.005" target="_blank" >10.1016/j.culher.2025.08.005</a>
Alternative languages
Result language
angličtina
Original language name
ForgAnoNet: A Neural Network for Anomaly Detection in Artworks Using X-ray and Visible Spectrum Imaging
Original language description
Forgery detection in paintings presents a significant challenge with substantial implications for the art world and forensic sciences. Given the high variability of artistic techniques and materials, forensic analysis must provide compelling, reproducible, and scientifically robust evidence. This paper introduces a novel technique for identifying anomalous regions in paintings, based on the detection of differences between visible and X-ray spectra, while also suppressing irrelevant artifacts, such as painting frames. Our model, the so-called ForgAnoNet, employs an architecture similar to O-Net but with several enhancements tailored to meet these specific needs. This architecture is the first to be applied to the fields of forensics and cultural heritage research. A methodology that is repeatable, accurate, and can suppress false detection from irrelevant irregularities. We proposed a novel neural network model that enhances both the precision and speed of detecting irregularities, such as cracks, voids, and previous restoration efforts. To evaluate the performance, we compared the methodology with five state-of-the-art models on the created datasets, which contained 4888 samples. A comprehensive evaluation of diverse X-ray images from various artworks demonstrates the effectiveness of our approach in practical applications. The newly developed ForgAnoNet achieves an accuracy of 98.08 %, significantly outperforming all other models in the study. Additionally, ForgAnoNet demonstrates precision, achieving a value of 0.4403, which effectively reduces false-positive rates and improves the reliability of anomaly detection in paintings. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
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
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
JOURNAL OF CULTURAL HERITAGE
ISSN
1296-2074
e-ISSN
1778-3674
Volume of the periodical
Volume
Issue of the periodical within the volume
76
Country of publishing house
FR - FRANCE
Number of pages
10
Pages from-to
29-38
UT code for WoS article
001573071000001
EID of the result in the Scopus database
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