A deep learning perspective on Rubens' attribution

TL;DR

This paper demonstrates how deep learning, specifically convolutional neural networks, can effectively assist in authenticating and attributing artworks by analyzing subtle stylistic features, exemplified through Rubens' paintings.

Contribution

It introduces a deep learning approach for art attribution, applying CNNs to distinguish Rubens' works from others, providing a computational tool to support art historical analysis.

Findings

High classification accuracy achieved

Deep learning reveals micro-level stylistic features

Supports traditional art historical methods

Abstract

This study explores the use of deep learning for the authentication and attribution of paintings, focusing on the complex case of Peter Paul Rubens and his workshop. A convolutional neural network was trained on a curated dataset of verified and comparative artworks to identify micro-level stylistic features characteristic of the master s hand. The model achieved high classification accuracy and demonstrated the potential of computational analysis to complement traditional art historical expertise, offering new insights into authorship and workshop collaboration.