用AI识别伪造画作，专注特定造假者风格。
Art Forgery Detection using Kolmogorov Arnold and Convolutional Neural Networks
- 构建专属伪造者数据集，用EfficientNet做多类分类。
- 模型对伪造画作判别一致，准确率超90%。
- 首次将KAN用于艺术伪造检测，适合鉴伪研究者。
艺术真伪鉴定长期依赖专家经验，但像沃尔夫冈·贝尔特拉奇这样的著名伪造者曾欺骗多位专家。近年来人工智能在图像处理中表现优异。本文提出一种针对贝尔特拉奇伪造作品的鉴别框架，不同于以往聚焦艺术家的AI方法，本研究专门分析特定伪造者。我们构建了一个包含已知艺术家被其伪造作品及伪造者真实作品的数据集，采用EfficientNet训练多类别图像分类模型，并与柯尔莫戈洛夫-阿诺德网络（KAN）进行对比，据我们所知，这是KAN首次应用于艺术领域。结果表明，不同模型对疑似伪造作品的预测高度一致，经视觉分析进一步验证了这些判断。
原文摘要 · Abstract (English)
Art authentication has historically established itself as a task requiring profound connoisseurship of one particular artist. Nevertheless, famous art forgers such as Wolfgang Beltracchi were able to deceive dozens of art experts. In recent years Artificial Intelligence algorithms have been successfully applied to various image processing tasks. In this work, we leverage the growing improvements in AI to present an art authentication framework for the identification of the forger Wolfgang Beltracchi. Differently from existing literature on AI-aided art authentication, we focus on a specialized model of a forger, rather than an artist, flipping the approach of traditional AI methods. We use a carefully compiled dataset of known artists forged by Beltracchi and a set of known works by the forger to train a multiclass image classification model based on EfficientNet. We compare the results with Kolmogorov Arnold Networks (KAN) which, to the best of our knowledge, have never been tested in the art domain. The results show a general agreement between the different models' predictions on artworks flagged as forgeries, which are then closely studied using visual analysis.
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