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.
@article{arxiv.2511.22667,
title = {A deep learning perspective on Rubens' attribution},
author = {A. Afifi and A. Kalimullin and S. Korchagin and I. Kudryashov},
journal= {arXiv preprint arXiv:2511.22667},
year = {2025}
}