Painter Verification Using Color Palettes: An Exploratory Study

Forgery detection in fine art necessitates collaboration among art historians, conservators, scientists, and forensic experts. Traditional methods, which rely on expert visual assessment and scientific analysis, are effective but often time-consuming and costly. Recent advancements in technology have introduced image analysis and machine learning techniques, offering efficient and precise alternatives for detecting art forgeries. Excluding material and chemical-based clues, assessing the authenticity of paintings can be broadly modeled along three visual dimensions: color, brushstrokes, and contents. This paper examines the efficacy of using color as a feature for determining the authenticity of paintings. We utilize machine learning algorithms to analyze the color palettes of over 100,000 digital images from approximately 1,500 artists. We compactly represented paintings through their color palettes and different other color-based features. Our experiments, designed as verification tasks, explore the potential of color-based features to verify the authorship of the artworks