'Images that fool computer vision raise security concerns' by Bill Steele in Cornell Chronicle

"Computers are learning to recognize objects with near-human ability. But Cornell researchers have found that computers, like humans, can be fooled by optical illusions, which raises security concerns and opens new avenues for research in computer vision.

Cornell graduate student Jason Yosinski and colleagues at the University of Wyoming Evolving Artificial Intelligence Laboratory have created images that look to humans like white noise or random geometric patterns but which computers identify with great confidence as common objects. They will report their work at the IEEE Computer Vision and Pattern Recognition conference in Boston June 7-12.

“We think our results are important for two reasons,” said Yosinski. “First, they highlight the extent to which computer vision systems based on modern supervised machine learning may be fooled, which has security implications in many areas. Second, the methods used in the paper provide an important debugging tool to discover exactly which artifacts the networks are learning.” "

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