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，只要经过充分训练，人工智能就能以显著准确性区分特定艺术家的真迹画作与人为赝品。然而，由于现有已知赝品数量有限，用于赝品检测的数据增强方法极具研究价值。本研究探究了将合成艺术品纳入训练数据集以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，并首次发布专用于赝品检测的数据集。为验证结论的普适性，我们对艺术家阿梅代奥·莫迪利亚尼和拉斐尔进行了同样分析。我们训练分类器区分原作与赝品，采用人为制作的赝品及仿知名艺术家风格的临摹品，同时用Stable Diffusion和StyleGAN生成的类似风格图像扩充训练集。实验发现，额外添加的合成赝品能持续提升人为赝品的检测效果。此外，与先前研究一致，将合成赝品纳入训练还可实现AI生成赝品的检测，尤其当赝品由相近生成器制作时效果更佳。