This painting went missing 20 years ago. Police tracked it down with the help of AI

Art theft is a global problem and police forces are struggling to keep on top of it. Now they are turning to AI software that snoop out the most cleverly hidden artworks.

During their routine visit to the annual antiquity fair in Parma, Italy, in October 2024, police officers busily snapped photos of the artworks as they walked between the stands. It wasn't art appreciation – it was detective work.

The images were uploaded to an internal database as the officers smiled at the vendors and wandered amid fist-sized bronze statuettes, patinated goblets, yellowing globes, and an endless assortment of oil paintings.

Just a couple of hours later, though, the police system flagged one of the snapshots with an alert signal: a painting of a young, fair-skinned woman wearing aquamarine and red, holding up a half-unravelled parchment scroll.       

The next day, officers rushed to the scene and seized the painting for their art experts to take a second look. The scroll in the painting was now, indeed, giving the investigators reason to be suspicious, says Lt Col Salvatore Rapicavoli, of the police unit for the Protection of Cultural Heritage of Italy. "There was something off."

While Rapicavoli's team has specialised in tracking down stolen artworks nationwide for decades, he says that the novel, tailor-made artificial intelligence system they were testing at the fair has massively sharpened their craft. It has since allowed the team to detect stolen art more quickly and helped them to spot details beyond what the human eye can normally see.

"There has been an exponential increase in the number of hits," says Rapicavoli. Since its introduction in November 2023, the AI system has helped his team recover more than 300 large plundered paintings and statues, and more than 165,000 smaller coins, medals, letters, manuscripts, religious objects, stamps and cards worth more than a total of €203m (£170m, $226m). The advances in Italy – in technique and policy – have been paving the way for machine learning in art theft investigations worldwide, promising to revolutionise the global crackdown on art heisters.                    

And while a human would likely have glossed over that woman holding a parchment scroll at first glance – nothing but a couple of darker shadows looking off balance – the team now had an active investigation on their hands.

Since 1969, the Italian police have kept a database of looted cultural artefacts across the nation – everything from gigantic oil paintings ripped from church altars in the night to ancient Roman and Etruscan artefacts excavated illegally. The database – the largest in the world, constantly fed by reports from cultural police authorities and customs – contains more than one million items. Though only some of the entries have pictures of the original artefacts.                     

Historically, to find stolen artworks, Italian police routinely visited fairs, antique shops, and upscale art auctions and compared what they saw there with millions of records back in their offices, first in paper form, then digitised in an online database. One by one, they would look for matches in the online repository by searching keywords, querying with details like "painting of woman with scroll".

This was time-consuming and clumsy, though, says Rapicavoli. Even if a sale looked suspicious, an officer scrolling through hundreds of database photos for a match could often miss key details. And the quality of the original artefact photos left a lot to be desired. The images were often taken decades earlier, in black and white, far away, on someone's dining room wall and littered with light blowouts, obscured areas, or extreme contrast.

"I asked myself how technology could get around this problem," says Rapicavoli, an IT engineer who joined the Italian police's stolen art team in 2013. "At least in the massive search-and-comparison phase." 

In 2020, his team conceived the AI algorithm called the Stolen Works of Art Detection System (Swoads). "The machine doesn't get bored," says Rapicavoli. Crucially, it can match hidden visual fingerprints in the works – pairing similarities between shades of colour and rough silhouettes.   

It can also spot segments of paintings that have been chopped up and rearranged – as is often the case with large altarpieces sold in separate slivers – or works........

© BBC