标题： 人工智能与艺术中的虚假信息：视觉语言模型能判断画布背后是人还是机器吗？ 显示英文标题

标题： Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?

摘要： 艺术作品的归属，尤其是绘画的归属，一直是艺术领域的一个问题。强大的人工智能模型能够生成和分析图像，这给绘画归属带来了新的挑战。一方面，人工智能模型可以创建模仿画家风格的图像，这可能导致错误的归属，例如被其他人工智能模型错误地归属。另一方面，人工智能模型可能无法正确识别真实绘画的艺术家，导致用户错误地归属绘画。在本文中，使用最先进的图像生成和分析人工智能模型，在一个包含接近40,000幅来自128位艺术家的绘画的大数据集上，对这两个问题进行了实验研究。结果表明，视觉语言模型在以下两个方面能力有限：1）进行画布归属；2）识别人工智能生成的图像。随着用户越来越多地依赖向人工智能模型查询来获取信息，这些结果表明需要提高视觉语言模型（VLMs）的能力，以可靠地执行艺术家归属和人工智能生成图像的检测，以防止错误信息的传播。 显示英文摘要

摘要： The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and analyze images creates new challenges for painting attribution. On the one hand, AI models can create images that mimic the style of a painter, which can be incorrectly attributed, for example, by other AI models. On the other hand, AI models may not be able to correctly identify the artist for real paintings, inducing users to incorrectly attribute paintings. In this paper, both problems are experimentally studied using state-of-the-art AI models for image generation and analysis on a large dataset with close to 40,000 paintings from 128 artists. The results show that vision language models have limited capabilities to: 1) perform canvas attribution and 2) to identify AI generated images. As users increasingly rely on queries to AI models to get information, these results show the need to improve the capabilities of VLMs to reliably perform artist attribution and detection of AI generated images to prevent the spread of incorrect information.