用深度学习分析鲁本斯画作笔触，辅助判断真伪与工作室分工
A deep learning perspective on Rubens' attribution
- 训练卷积神经网络识别鲁本斯作品的微尺度风格特征
- 模型分类准确率高，能有效区分大师亲笔与弟子代笔
- 适合艺术鉴定、数字人文研究者参考
本研究探讨深度学习在绘画真伪鉴定与作者归属中的应用，聚焦彼得·保罗·鲁本斯及其工作室这一复杂案例。通过在经验证的对比艺术品数据集上训练卷积神经网络，模型成功识别出体现大师个人风格的微观层面特征。实验结果表明，该方法具备高分类准确率，展示了计算分析在补充传统艺术史研究方面的潜力，为作者身份认定及工作室协作关系提供了新的洞见。
原文摘要 · Abstract (English)
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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