Instead of subjecting works to lengthy and hugely expensive materials analysis, hoping a forger has made a tiny slip — a stray fiber, varnish made using ingredients that wouldn’t have been available in 16th-century Venice — the new technique is so powerful that it doesn’t even need access to the original work: A digital photograph will do. Even more striking, this method is aided by artificial intelligence. A technology whose previous contributions to art history have consisted of some

At least that’s the theory, says Ahmed Elgammal, PhD, whose team at Rutgers has developed the new process, which was made public late last year. “It is still very much under development; we are working all the time. But we think it will be a hugely valuable addition to the arsenal.”

That theory is certainly intriguing. Instead of obsessing over materials, the new technique takes a hard look at the picture itself: Specifically, the thousands of tiny individual strokes that compose it.

Every single gesture — shape, curvature, the velocity with which a brush- or pencil-stroke is applied — reveals something about the artist who made it. Together, they form a telltale fingerprint. Analyze enough works and build up a database, and the idea is that you can find every artist’s fingerprint. Add in a work you’re unsure about, and you’ll be able to tell in minutes whether it’s really a Matisse or if it was completed in a garage in Los Angeles last week. You wouldn’t even need the whole work; an image of one brushstroke could give the game away.

“Strokes capture unintentional process,” explains Elgammal. “The artist is focused on composition, physical movement, brushes — all those things. But the stroke is the telltale sign.”

The paper Elgammal and his colleagues published last November examined 300 authentic drawings by Picasso, Matisse, Egon Schiele, and a number of other artists and broke them down into more than 80,000 strokes. Machine-learning techniques refined the data set for each artist; forgers were then commissioned to produce a batch of fakes. To put the algorithm though its paces, the forgeries were fed into the system. When analyzing individual strokes, it was over 70 percent accurate; when whole drawings were examined, the success rate increased to over 80 percent. (The researchers claim 100 percent accuracy “in most settings.”)

The researchers are so confident that they included images of originals and fakes alongside each other in the published paper, daring so-called experts to make up their own minds. (Reader, I scored dismally.) One of Elgammal’s colleagues, Dutch painting conservator Milko den Leeuw, compares it to the way we recognize family members: They look similar, but we’re just not sure why. “Take identical twins,” he says. “Outsiders can’t separate them, but the parents can. How does that work? It’s the same with a work of art. Why do I recognize that this is a Picasso and that isn’t?”

The idea of fingerprinting artists via their strokes actually dates back to the 1950s and a technique developed by Dutch art historian Maurits Michel van Dantzig. Van Dantzig called his approach “pictology,” arguing that because every work of art is a product of the human hand, and every hand is different, it should be possible to identify authorship using these telltale strokes.

The problem, though, was that there was too much data. Even a simple drawing contains hundreds or even thousands of strokes, all of which needed to be examined by the human eye and catalogued. Multiply that by every work, and you see how impractical it was.

“It just wasn’t possible to test it,” says den Leeuw, who first became aware of pictology as a student. “I saw many attempts, but mostly it ended in ideas that would never be.”

But can A.I. now do what humans failed to, and give an art historian’s trained eye some sort of scientific basis? “Exactly,” says den Leeuw. “Very often it’s a gut feeling. We’re trying to unpick the mystery.”

Though Mass says she’s unlikely to throw out her fluorescence gun just yet, she admits to being impressed. “A lot of people in the field are excited by A.I. It’s not a magic bullet, but it’ll be another tool. And it’s really valuable when you’re dealing with a sophisticated forger who’s got everything else right — paint, paper, filler, all the materials.”