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Video about detecting art forgeries radioactive dating:

3 Ways Science Can Bust Art Forgeries




Detecting art forgeries radioactive dating

Detecting art forgeries radioactive dating


Then a deep recurrent neural network RNN learned what features in the strokes were important to identify the artist. With both algorithms working in tandem, the researchers were able to correctly identify artists around 80 percent of the time. Incorrect email format By signing up you agree to receive email newsletters and notifications from MIT Technology Review. 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. 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. The researchers also trained a machine-learning algorithm to look for specific features, like the shape of the line in a stroke. This gave them two different techniques to detect forgeries, and the combined method proved powerful. Sign Up Thank you — please check your email to complete your sign up. 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. Art historians might bring a suspect work into a lab for infrared spectroscopy, radiometric dating, gas chromatography, or a combination of such tests.

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Detecting art forgeries radioactive dating. This AI Can Spot Art Forgeries by Looking at One Brushstroke.

Detecting art forgeries radioactive dating


Then a deep recurrent neural network RNN learned what features in the strokes were important to identify the artist. With both algorithms working in tandem, the researchers were able to correctly identify artists around 80 percent of the time. Incorrect email format By signing up you agree to receive email newsletters and notifications from MIT Technology Review. 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. 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. The researchers also trained a machine-learning algorithm to look for specific features, like the shape of the line in a stroke. This gave them two different techniques to detect forgeries, and the combined method proved powerful. Sign Up Thank you — please check your email to complete your sign up. 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. Art historians might bring a suspect work into a lab for infrared spectroscopy, radiometric dating, gas chromatography, or a combination of such tests.

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2 thoughts on “Detecting art forgeries radioactive dating

  1. [RANDKEYWORD
    Nikokree

    This technique can only be used when lines are obvious, so for paintings where brushstrokes are made invisible, it is no help. 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.

  2. [RANDKEYWORD
    Vujind

    The system was able to identify the forgeries in every instance, simply by looking at a single stroke. The researchers also trained a machine-learning algorithm to look for specific features, like the shape of the line in a stroke.

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