[2603.04343] Enhancing Authorship Attribution with Synthetic Paintings

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04343 (cs) [Submitted on 4 Mar 2026] Title:Enhancing Authorship Attribution with Synthetic Paintings Authors:Clarissa Loures, Caio Hosken, Luan Oliveira, Gianlucca Zuin, Adriano Veloso View a PDF of the paper titled Enhancing Authorship Attribution with Synthetic Paintings, by Clarissa Loures and 3 other authors View PDF HTML (experimental) Abstract:Attributing authorship to paintings is a historically complex task, and one of its main challenges is the limited availability of real artworks for training computational models. This study investigates whether synthetic images, generated through DreamBooth fine-tuning of Stable Diffusion, can improve the performance of classification models in this context. We propose a hybrid approach that combines real and synthetic data to enhance model accuracy and generalization across similar artistic styles. Experimental results show that adding synthetic images leads to higher ROC-AUC and accuracy compared to using only real paintings. By integrating generative and discriminative methods, this work contributes to the development of computer vision techniques for artwork authentication in data-scarce scenarios. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2603.04343 [cs.CV]   (or arXiv:2603.04343v1 [cs.CV] for this version)   https://doi.org/10.48550/arXiv.2603.04343 Focus to learn more arXiv-issued DOI v...