Since the machine-learning algorithm was trained on specific features, the difference between it and the RNN probably points to the characteristics the neural network was looking at to detect forgeries. In this case, it was using the changing strength along a stroke—that is, how hard an artist was pushing, based on the weight of the line—to identify the artist. With both algorithms working in tandem, the researchers were able to correctly identify artists around 80 percent of the time.

The researchers also commissioned artists to create drawings in the same style as the pieces in the data set to test the system’s ability to spot fakes. The system was able to identify the forgeries in every instance, simply by looking at a single stroke.

“A human cannot do that,” says Ahmed Elgammal, a professor at Rutgers and one of the paper’s authors.

This technique can only be used when lines are obvious, so for paintings where brushstrokes are made invisible, it is no help. But to further validate their results, Elgammal says, they plan to test the method on Impressionist works and other 19th-century art where brushstrokes are clear.

The most promising part of the research might be the way the researchers used the second method to make clear what the RNN is doing, says Eric Postma at Tilburg University in the Netherlands, who has done work in detecting art forgeries with AI for more than a decade. There could be more applications for artificial intelligence in art, he says, but art historians and researchers, steeped in centuries of tradition, have been slow to embrace such techniques. That’s in part because it can be difficult to understand how a machine arrived at its results—a problem this latest research could help solve.