by marinajunadart | 2025-07-12 16:16:25+00:00
International art-tech company ArtCollecting has dedicated its resources to analyzing around 100 works from Nina Moleva’s collection. The study was conducted using a custom-developed algorithm. The machine learning process was overseen by cultural heritage expert and ArtCollecting director Marina Nadeeva, in collaboration with developers.

Nina Moleva’s collection is of particular interest because the owner herself valued it at $2 billion. According to Moleva, the wife of nonconformist artist Ely Belyutin, their holdings include works by Leonardo da Vinci, Michelangelo, Titian, Van Dyck, Monet, and even Natalia Goncharova. If true, this would be the largest private collection of world-class masterpieces.

Now, researchers have a chance to unravel the mystery. Marina Nadeeva, a cultural heritage expert, examined part of the collection using ArtCollecting’s innovative AI alongside traditional art analysis methods, including comparative analysis, stylistic assessment, and technical examination. The study focused on works by Ely Belyutin, 16th–19th-century icons, and paintings and graphic works attributed to Claude Monet, Édouard Manet, and Natalia Goncharova. The algorithm analyzed not only visual features but also paint microstructure, signs of aging, and even hidden layers—details that might escape the human eye.

Recovering the Lost Image: AI Restores an Icon from Nina Moleva’s Collection

A custom algorithm was created specifically for this study, capable of processing and cross-referencing vast datasets. The system analyzed information from over 100,000 icons (15th–19th centuries) produced in provincial workshops, drawing from museum catalogs, antique dealer archives, historical records, and obscure private collections.

The AI not only reconstructed the icon’s likely original appearance but also identified works with similar stylistic and technical features—an especially valuable contribution, given that provincial iconography remains a "blank spot" in art history. Unlike major icon-painting centers, provincial workshops rarely documented their output, and their surviving works are scattered across small regional museums and private collections.

Traditionally, such research required years of work by art historians, restorers, and chemical analysts. Now, AI accelerates the process dramatically, analyzing enormous datasets in days. Crucially, the technology doesn’t replace experts—it empowers them with advanced analytical tools.

The most striking achievement, however, was the AI-assisted reconstruction of damaged elements. Since iconography follows strict compositional rules, recurring motifs allowed the algorithm to compare the damaged areas with thousands of analogues—proposing the most probable original composition.

Authenticating a Work Attributed to Édouard Manet

Experts have begun verifying the authenticity of a painting attributed to Édouard Manet using artificial intelligence. A specialized algorithm analyzes the artist’s brushwork by comparing it to verified masterpieces. The neural network has already been trained on open-access databases from the Musée d'Orsay, the Musée Marmottan Monet in Paris, and the National Gallery of Art in Washington. Researchers are now refining the analysis by studying Manet’s drawings and watercolors to cross-reference details. Simultaneously, the algorithm is being adjusted to identify other potential authors should the attribution to Manet prove incorrect.

Microscopic technical analysis has helped narrow down the likely timeframe of the painting’s creation. Additionally, archival research in Russian libraries—including French publications that entered Imperial Russia and later the USSR—has been completed. Investigators examined records dating back to 1912 for further clues on provenance. The study is ongoing, with new findings expected soon.