The computer says there is an 80.58% probability that the painting is a real Renoir

Gazing enigmatically at an unseen object to her right, the black-haired woman bears a striking resemblance to the person depicted in Pierre-Auguste Renoir’s painting Gabrielle, which Sotheby’s recently valued at between £100,000-150,000.

But art connoisseurs disagree about whether the work, owned by a private Swiss collector, is the real deal. Now artificial intelligence has waded in to help settle the dispute, and the computer has deemed it probably a genuine Renoir.

AI is increasingly being used to judge whether valuable works of art are genuine or fake. Earlier this month, Art Recognition, the Swiss company that developed the technique, announced that it had determined that Switzerland’s only Titian – a work titled Evening Landscape with Couple, held by the Kunsthaus Zürich – was probably not painted by the 16th-century Venetian artist .

Still, art connoisseurs have warned that the AI ​​is only as good as the paintings it’s trained on. If they are fake, or contain areas that have been patched, it can create even more uncertainty.

Art Recognition was contacted about Renoir, titled Portrait de femme (Gabrielle), after the Wildenstein Plattner Institute – one of two institutes that publish a comprehensive list of all known works of art by Renoir, known as a catalogs raisonnés – refused to include it in its list.

The company used photographic reproductions of 206 authentic paintings by the French Impressionist to teach its algorithm about his style, which to human observers is characterized by broken brushstrokes and bold combinations of complementary colors. To increase precision, it also split the images into smaller patches and showed these to the algorithm, and trained it on a selection of paintings by artists with a similar style who were active around the same time as Renoir.

Based on this assessment, it concluded that there was an 80.58% chance that Portrait de femme (Gabrielle) was painted by Renoir.

Carina Popovici, Art Recognition’s CEO, believes that this ability to put a number on the degree of uncertainty is important. Speaking at a meeting on the use of forensics and technology in the art trade at the Art Loss Register in London on Monday, she said: “Art owners are often told by connoisseurs that it is their ‘impression’ or ‘intuition’ that a painting is genuine or not , which can be very frustrating. They really appreciate us being more precise.”

Encouraged by this result, the painting’s owner contacted another Parisian expert group, GP.F.Dauberville & Archives Bernheim-Jeune, which publishes its own catalogs raisonnés of works by Renoir. After requesting a scientific analysis of the pigments in the painting, they too concluded that it was a genuine Renoir.

Dr Bendor Grosvenor, art historian and presenter of BBC Four’s Britain’s Lost Masterpieces, worried that such technology could devalue the contribution of experts in judging a work of art’s authenticity.

“So far, the methods used to ‘train’ the AI ​​programs, and the fact that they say they can judge an attribution just from an iPhone photo, are unimpressive,” he said.

“The technique is particularly weak in its inability to account for a painting’s condition – so many old master paintings are damaged and disfigured by layers of dirt and overpainting that, without forensic inspection, make it difficult to discern what is and is not original.

“If any human art appraiser offered to provide a ‘certificate of authenticity’ costing thousands of dollars based on nothing more than an iPhone photo and a partial knowledge of an artist’s work, they would be laughed at.”

Popovici agreed that the quality of the training dataset was critical and said they went to great lengths to ensure they only use photographs of authentic artwork. So far, they’ve trained their AI to recognize about 300 artists, including most of the French Impressionists and Old Master painters.

“We understand that connoisseurs may feel threatened by this technology, but we’re not trying to push them out of the way,” Popovici said.

“We really want to give them the ability to use this system to help them make a decision, maybe in cases where they’re not so sure. But for that to happen, they have to be open to this technology.”

Julian Radcliffe, chairman of the Art Loss Register, which maintains the world’s largest private database of stolen art, antiques and collectables, said: “Artificial intelligence is playing an increasing role in helping to authenticate art but it needs to be linked to the expertise of experts who are specialized in the artist, well-established science such as pigment analysis and provenance research.

“Its advantage lies in its ability to give yes/no answers to, for example, pattern analysis or matching and to constantly improve, but its work must be interpreted by a human who must have asked the right question.

“The pursuit of absolute security in authentication has not been, and may never be, achieved – but we are getting closer.”

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