Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network

This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure networks in paintings. Craquelure, the fine pattern of the craquelure network formed on a painting’s surface over time, is a unique “fingerprin...

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Vydané v:Applied sciences Ročník 15; číslo 16; s. 9014
Hlavní autori: Chirosca, Gianina, Radvan, Roxana, Pop, Matei, Chirosca, Alecsandru
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2025
Predmet:
Climate change
Computational linguistics
Computer vision
convolutional neural networks
Cracks
craquelure detection
Cultural heritage
Deep learning
image processing
Language processing
Machine learning
Morphology
Natural language interfaces
Neural networks
ISSN:2076-3417, 2076-3417
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Abstract This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure networks in paintings. Craquelure, the fine pattern of the craquelure network formed on a painting’s surface over time, is a unique “fingerprint” for artwork item authentication. The system utilizes a modified VGG19 backbone, which effectively balances computational efficiency with the ability to extract rich, multi-scale features from high-resolution grayscale images. By leveraging this architecture, the model captures global structural patterns and local texture information, which are essential for reliable analysis.
AbstractList This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure networks in paintings. Craquelure, the fine pattern of the craquelure network formed on a painting’s surface over time, is a unique “fingerprint” for artwork item authentication. The system utilizes a modified VGG19 backbone, which effectively balances computational efficiency with the ability to extract rich, multi-scale features from high-resolution grayscale images. By leveraging this architecture, the model captures global structural patterns and local texture information, which are essential for reliable analysis.
Audience Academic
Author Pop, Matei
Radvan, Roxana
Chirosca, Alecsandru
Chirosca, Gianina
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SubjectTerms Climate change
Computational linguistics
Computer vision
convolutional neural networks
Cracks
craquelure detection
Cultural heritage
Deep learning
image processing
Language processing
Machine learning
Morphology
Natural language interfaces
Neural networks
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Title Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network
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