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.

Could artificial intelligence be the end of the dubious science of connoisseurship? According to a new study, a form of AI called a recurrent neural network may now be able to identify forged paintings. Researchers from New Jersey's Rutgers University and the Atelier for Restoration & Research of Paintings in the Netherlands have published their findings in a paper, titled "Picasso, Matisse, or a Fake? The AI was able to find fake artworks simply by comparing the strokes used to compose the image. Determining the authenticity of a work of art has long been a considerable challenge.

Detecting art forgeries is hard and expensive. Art historians might bring a suspect work into a lab for infrared spectroscopy, radiometric dating, gas chromatography, or a combination of such tests. AI, it turns out, doesn't need all that: it can spot a fake just by looking at the strokes used to compose a piece. In a new paper, researchers from Rutgers University and the Atelier for Restoration & Research of Paintings in the Netherlands document how their system broke down almost 300 line drawings by Picasso, Matisse, Modigliani, and other famous artists into 80,000 individual strokes. Then a deep recurrent neural network (RNN) learned what features in the strokes were important to identify the artist.