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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.04343 |
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| _version_ | 1866910041131450368 |
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| author | Loures, Clarissa Hosken, Caio Oliveira, Luan Zuin, Gianlucca Veloso, Adriano |
| author_facet | Loures, Clarissa Hosken, Caio Oliveira, Luan Zuin, Gianlucca Veloso, Adriano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04343 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Authorship Attribution with Synthetic Paintings Loures, Clarissa Hosken, Caio Oliveira, Luan Zuin, Gianlucca Veloso, Adriano Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Enhancing Authorship Attribution with Synthetic Paintings |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2603.04343 |