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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Veröffentlicht in:Applied sciences Jg. 15; H. 16; S. 9014
Hauptverfasser: Chirosca, Gianina, Radvan, Roxana, Pop, Matei, Chirosca, Alecsandru
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.08.2025
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15169014