人工智能与艺术中的虚假信息：视觉语言模型能否辨别画作背后的手笔与机器？

摘要

艺术品尤其是绘画作品的归属问题，历来是艺术领域的一大难题。随着能够生成和分析图像的强大人工智能模型的出现，绘画作品的归属认定面临新的挑战。一方面，AI模型能够创作出模仿特定画家风格的图像，这些图像可能会被其他AI模型错误地归因；另一方面，AI模型在识别真实画作的作者时也可能出现偏差，导致用户对画作进行错误的归属认定。本文利用最先进的图像生成与分析AI模型，在一个包含128位艺术家近40,000幅画作的大型数据集上，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方面能力有限：1）进行画布归属认定；2）识别AI生成的图像。随着用户越来越依赖向AI模型查询获取信息，这些结果凸显了提升视觉语言模型可靠执行艺术家归属认定及AI生成图像检测能力的必要性，以防止错误信息的传播。

English

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.

人工智能与艺术中的虚假信息：视觉语言模型能否辨别画作背后的手笔与机器？

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

摘要

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