Previous research has shown that Artificial Intelligence is capable of distinguishing between authentic paintings by a given artist and human-made forgeries with remarkable accuracy, provided sufficient training. However, with the limited amount of existing known forgeries, augmentation methods for forgery detection are highly desirable. In this work, we examine the potential of incorporating synthetic artworks into training datasets to enhance the performance of forgery detection. Our investigation focuses on paintings by Vincent van Gogh, for which we release the first dataset specialized for forgery detection. To reinforce our results, we conduct the same analyses on the artists Amedeo Modigliani and Raphael. We train a classifier to distinguish original artworks from forgeries. For this, we use human-made forgeries and imitations in the style of well-known artists and augment our training sets with images in a similar style generated by Stable Diffusion and StyleGAN. We find that the additional synthetic forgeries consistently improve the detection of human-made forgeries. In addition, we find that, in line with previous research, the inclusion of synthetic forgeries in the training also enables the detection of AI-generated forgeries, especially if created using a similar generator.

翻译：先前研究表明，人工智能在充分训练后能够以显著精度区分特定艺术家的真迹与人为赝品。然而，鉴于现存已知赝品数量有限，亟需开发针对赝品检测的数据增强方法。本研究探讨将合成艺术品纳入训练数据集对提升赝品检测性能的潜力。我们以文森特·梵高的画作为核心研究对象，并首次发布了专用于赝品检测的数据集。为强化结论可靠性，我们对阿梅代奥·莫迪利亚尼和拉斐尔两位艺术家开展了相同分析。我们训练分类器区分原创作品与赝品，使用人为赝品及模仿知名艺术家风格的仿作，并通过Stable Diffusion和StyleGAN生成的相似风格图像扩充训练集。研究发现，额外添加的合成赝品可显著提升人为赝品的检测效果。此外，与先前研究一致，在训练中纳入合成赝品还能实现对AI生成赝品的检测——尤其当此类赝品由类似生成器创建时效果更佳。