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.

翻译：绘画作品的作者归属判定历来是一项复杂的任务，其主要挑战之一在于可用于训练计算模型的真实艺术品数量有限。本研究探讨了通过DreamBooth微调Stable Diffusion生成的合成图像，能否在此背景下提升分类模型的性能。我们提出一种结合真实数据与合成数据的混合方法，旨在提高模型在相似艺术风格间的准确性与泛化能力。实验结果表明，与仅使用真实绘画相比，添加合成图像能带来更高的ROC-AUC值与准确率。通过融合生成式与判别式方法，本研究为数据稀缺场景下的艺术品认证计算机视觉技术发展提供了新的思路。