https://doi.org/10.1140/epjp/s13360-025-07212-0
Regular Article
The convergence of artificial and human intelligence in art authentication: a perspective on machine learning applications
1
Faculty of Chemistry, University of Wrocław, F. Joliot-Curie 14, 50-383, Wrocław, Poland
2
Hugo Steinhaus Center, Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370, Wrocław, Poland
3
Faculty of Fine Arts, Nicolaus Copernicus University, Gagarina 7, 87-100, Toruń, Poland
4
Institute of Physics, Nicolaus Copernicus University, Grudziądzka 5, 87-100, Toruń, Poland
a
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Received:
20
January
2025
Accepted:
13
December
2025
Published online:
29
January
2026
The intersection of machine learning and art authentication has emerged as a transformative area within the field of art analysis. This paper explores the application of various machine learning techniques to enhance the efficiency of art authentication processes. Two procedures with potential use in the identification of forgeries are discussed. The supervised one uses attribution markers collected in an extensive analysis of paintings as input to a classification model. The resulting classifier should aid an art expert in the final assessment of authenticity. The unsupervised method is easier to carry out, as it does not require labeled training data. It may help to identify forged artworks as outliers in the dataset by measuring their similarities to authentic objects. The methods are tested on paintings attributed to M. Willmann and A. Grottger, respectively. Our findings open up new avenues for research and exploration at the intersection of the art world and machine learning. They also emphasize the importance of a collaborative approach that integrates traditional art historical expertise with advanced computational methods, thereby enriching the understanding of artworks and enhancing the efficacy of authentication practices.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
