The persistence of painting styles

TL;DR

This paper demonstrates how persistent homology, a topological data analysis method, can objectively differentiate artistic styles, artists, and distinguish AI-generated images from authentic artworks with statistical certainty.

Contribution

It introduces the application of persistent homology to analyze and classify artistic styles, providing a structured, quantitative approach to art analysis.

Findings

PH can differentiate between artists and artistic currents.

PH distinguishes AI-generated images from real artworks.

Statistical certainty supports the robustness of the method.

Abstract

Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Traditionally, identifying artistic styles has been the job of art historians or critics, relying on visual intuition and experience. However, with the advancement of mathematical tools, we can explore art through more structured lens. In this work, we show how persistent homology (PH), a method from topological data analysis, provides objective and interpretable insights on artistic styles. We show how PH can, with statistical certainty, differentiate between artists, both from different artistic currents and from the same one, and distinguish images of an artist from an AI-generated image in the artist's style.