Successes in computer vision
Tomaso Poggio, co-director of the Center for Biological and Computational Learning at the Massachusetts Institute of Technology in Cambridge, said he wasn’t surprised that Keren’s computer program performed as well as it did in recognizing the paintings. Among the more recent successes in computer vision, Poggio said his group and others have created models that even replicated the first steps of human vision, as the initial wave of information is captured by the eye’s retina and sent up to the brain.

In Poggio’s test of that “immediate perception,” his computer model matched volunteers’ ability to detect whether an animal was present in a series of quickly flashed landscapes or cityscapes.  “I don’t see why computer vision cannot do as well, eventually, as people do,” he said. “Whether it can do better depends on the task.”

Computers are particularly adept at recognizing fingerprints, for example. But forgeries? Poggio said he believes computers could become as reliable as art experts someday,  but he doubts that a reliance on simple vision will ever yield perfection.

Beyond the mere act of seeing, humans can look around and describe a scene. Similarly, Poggio said detecting a forgery would require seeing the painting but also analyzing the artist’s brushwork, understanding the historical timeframe in which it was completed and other reasoned considerations. Detection, then, would require the equivalent of human intelligence, which Poggio conceded said is still far from being replicated in the laboratory.

Art specialists gathered in Boston earlier this month at the annual conference of the American Association for the Advancement of Science greeted the new research cautiously, noting the high bar that scientific techniques must clear to be accepted in a court of law, much less by other experts.

A high-stakes game
Researchers have struggled for years to sort out true Rembrandts from copies, for example, a task complicated by the artist’s propensity to switch styles and to encourage his best students to imitate him.

Last year, Narayan Khandekar, a senior conservation scientist at Harvard University’s Straus Center for Conservation, and two colleagues concluded that at least three disputed Jackson Pollock paintings likely aren’t authentic. But their report,  based on a technical analysis of pigments in the artworks that suggested several weren’t commercially available until decades after Pollock’s death in 1956, only seemed to fuel the controversy.

The stakes may be especially high for artworks that, if authenticated, could earn their owners millions, said Jessica Darraby, a Los Angeles-based attorney, gallery owner and art specialist. “The art market is the hottest market in the world,” she said. “For a Pollock to be a Pollock in 2008 is a very big financial deal.”

Given the consequences, new validation methods will likely receive extreme scrutiny and no one method is likely to emerge as a “be-all, end-all approach,” Darraby said.

Nevertheless, Harvard’s Khandekar said he saw the value of trying to complement existing techniques with one based on computer vision. “I’m sure it will have potential,” he said, though he warned,  “it’s in its infancy now.”

Could a computer ever be sophisticated enough to  “appreciate” good art?

“Oh, boy,” Keren said, and laughed. Mathematical tools can certainly tell him if a student’s program has been designed well, he said. Then again, there’s a rather big gap between that and, say, quantifying the merits of van Gogh’s famous “Starry Night” or a classic novel such as “Crime and Punishment.”

“Frankly, I think we’re quite far from that,” Keren concluded.

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