Work

Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network

Year: 2025

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app15169014

Authors: Gianina Chirosca, Roxana Radvan, Matei Pop, Alecsandru Chirosca

Institutions: National Institute of Research and Development for Optoelectronics, University of Bucharest

Language: English

Topic: Image Processing and 3D Reconstruction

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Ministerul Cercetării, Inovării şi Digitalizării