Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network

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.

Keywords

• craquelure detection
• convolutional neural networks
• image processing