{"ID":2836082,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22667","arxiv_id":"2511.22667","title":"A deep learning perspective on Rubens' attribution","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.","short_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 characteri...","url_abs":"https://arxiv.org/abs/2511.22667","url_pdf":"https://arxiv.org/pdf/2511.22667v1","authors":"[\"A. Afifi\",\"A. Kalimullin\",\"S. Korchagin\",\"I. Kudryashov\"]","published":"2025-11-27T18:01:24Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}