人工智慧與藝術中的虛假資訊：視覺語言模型能否辨別畫作背後是人手還是機器？

摘要

藝術作品，尤其是繪畫的歸屬問題，一直是藝術領域中的一大難題。隨著能夠生成和分析圖像的強大人工智慧模型的出現，繪畫歸屬面臨了新的挑戰。一方面，AI模型可以創作出模仿特定畫家風格的圖像，這些圖像可能會被其他AI模型錯誤地歸屬。另一方面，AI模型可能無法正確識別真實繪畫的藝術家，導致使用者錯誤地歸屬作品。本文利用最先進的AI圖像生成和分析模型，在包含近40,000幅來自128位藝術家的繪畫的大型數據集上，對這兩個問題進行了實驗研究。結果顯示，視覺語言模型在以下兩方面的能力有限：1) 進行畫布歸屬，2) 識別AI生成的圖像。隨著使用者越來越依賴向AI模型查詢以獲取資訊，這些結果表明有必要提升視覺語言模型的能力，以可靠地執行藝術家歸屬和AI生成圖像的檢測，從而防止錯誤資訊的傳播。

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.

人工智慧與藝術中的虛假資訊：視覺語言模型能否辨別畫作背後是人手還是機器？

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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