collision detection: The computerized critic

June 14, 2004

The computerized critic

Recently, I wrote a piece about an art forger whose copies of Gaugin, Monet, and other modernists were good enough to fool experts in several countries. But could they fool a neural-net trained on the artists' work?

That's what computer scientist Eric Postma is trying to find out. He's created an artificial-intelligence program called Authentic -- which can stare at hundreds of a painter's works, figure out regularities in his or her style, and then use that knowledge to deduce whether any particular painting is authentic. This also produces an interesting side effect: The program is able to spot patterns in an artist's technique that no human has noticed before. As the New York Times reports:

By analyzing several hundred low-resolution images of van Gogh paintings downloaded from the Web, the Authentic team still managed to pick out, in hours, patterns that would have taken much longer to detect using manual research � for example, the fact that van Gogh's use of complementary colors was greater when outlining human figures than it was for other objects in his paintings.
"As data, these Web images are terrible," said Mr. Berezhnoy. "Nevertheless, our method confirmed an increase in van Gogh's use of opponent colors. I could tell you that van Gogh started to use blue-yellow opponency in such and such year, and red-green later on, or that he used opponent color to highlight portrait silhouettes and human figures. Do you think I had time to draw those conclusions by studying all 854 paintings?"

That picture above is, by the way, Van Gogh's "Olive Grove".

Posted by Clive Thompson at June 14, 2004 11:24 PM

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Comments

Amazing stuff. When you think about all the levels of knowledge, skill and aesthetic choices that go into a piece of art, or for that matter, the level of complexity that goes into something like machine vision, an application that could do something like detect authenticity seems worlds away. But somehow, when it's stripped of all of that and reduced to data, finding a pattern in the data that would separate a pretender from the real thing isn't impossible.

In grad school a professor casually introduced neural nets as a way to analyze, then generate, data sets for music. He mentioned that these nets were being used as a pretty reliable indicator of the composer of origin (i.e. is this piece really a J.S. Bach, or a fake? No, not the guy from Skid Row).

What if Sakhai worked with Robert Peake (both earlier posts)? Talk about 'collision detection', huh?

Posted by: jason at June 15, 2004 10:24 AM

Excellent set of links to connectionist models of music, sir!

Posted by: Clive at June 15, 2004 10:38 AM

interesting additions to the toolbox of the fakebusters. i believe we are many many years from any machine learning capable of matching the sophisticated eye of a true expert. some very deep synthetic processes of analysis go into a "feel" for bogus works.

interestingly, the problem of developing an eye for fakery is complicated by the fact that there are so many fakes, in relation to the number of truly-authenticated works.

advances in technical analysis- of which i include mr. postma's efforts- will always make the archnacometrist's job easier. but when it comes down to it, sometimes it's all about gut feelings. thomas hoving, late of the new york met, described how famed art historian Bernard Berenson would sometimes react to fakes. that even in the absence of any scientific indications of dubiosity, berenson would say that his stomach felt wrong, that he felt a curious ringing in his ears, or that he was struck by a momentary depression.

Posted by: chris at June 15, 2004 8:28 PM

A momentary depression! That is terribly cool.

Posted by: Clive at June 24, 2004 11:01 PM

Fac ut gaudeam - Make my day

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