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.

翻译：艺术鉴定历来被视为一项需要深入理解特定艺术家风格的专业任务。然而，以沃尔夫冈·贝特拉奇为代表的著名艺术伪造者曾成功欺骗过数十位艺术专家。近年来，人工智能算法已在各类图像处理任务中得到成功应用。本研究借助人工智能技术的持续进步，提出一个用于识别伪造者沃尔夫冈·贝特拉奇的艺术鉴定框架。与现有AI辅助艺术鉴定的文献不同，我们聚焦于伪造者而非艺术家的专项模型，从而颠覆了传统AI方法的思路。我们使用精心构建的数据集进行训练，该数据集包含贝特拉奇伪造的已知艺术家作品集以及该伪造者本人的已知作品集，并基于EfficientNet构建了多类别图像分类模型。我们将结果与Kolmogorov Arnold网络（KAN）进行对比——据我们所知，后者从未在艺术领域进行过测试。结果显示，不同模型对被标记为赝品的艺术品预测结果总体一致，这些作品随后通过视觉分析进行了深入研析。