Artificial Intelligence and Misinformation in Art: Can Vision Language
  Models Judge the Hand or the Machine Behind the Canvas?
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.
6891f2119373cd501b6a2d6c
Tarian Fu
6891f2119373cd501b6a2d6d
Javier Conde
6891f2119373cd501b6a2d6e
Gonzalo Martínez
6891f2119373cd501b6a2d6f
Pedro Reviriego
6891f2119373cd501b6a2d70
Elena Merino-Gómez
6891f2119373cd501b6a2d71
Fernando Moral
Artificial Intelligence and Misinformation in Art: Can Vision Language
  Models Judge the Hand or the Machine Behind the Canvas?
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.
State-of-the-art vision language models struggle with accurately attributing artists and distinguishing AI-generated images, highlighting the need for improvement to prevent misinformation.
vision language models canvas attribution AI generated images