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arXiv · 2511.22667

A deep learning perspective on Rubens' attribution

A. Afifi·A. Kalimullin·S. Korchagin·I. Kudryashov
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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.

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A. Afifi, A. Kalimullin, S. Korchagin, I. Kudryashov. 2025-11-27. A deep learning perspective on Rubens' attribution. https://arxiv.org/abs/2511.22667

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