A deep learning perspective on Rubens' attribution
This addresses the challenge of authorship verification in art history, particularly for complex cases like Rubens' workshop, though it is incremental in applying existing deep learning methods to a new domain.
The study tackled the problem of authenticating and attributing paintings by Peter Paul Rubens and his workshop using deep learning, achieving high classification accuracy to identify stylistic features and complement traditional art historical expertise.
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.