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What is adversarial machine learning?

24/07/2020

To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as “gibbon.” And oddly enough, it had more confidence in the gibbon image. The algorithm in question was...
To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as “gibbon.” And oddly enough, it had more confidence in the gibbon image. The algorithm in question was GoogLeNet, a convolutional neural network architecture that won the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2014). The right image is an “adversarial example.” It has undergone subtle manipulations that go unnoticed to the human eye while making it a totally different sight to the digital eye of a  machine learning… This story continues at The Next Web To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as “gibbon.” And oddly enough, it had more confidence in the gibbon image. The algorithm in question was GoogLeNet, a convolutional neural network architecture that won the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2014). The right image is an “adversarial example.” It has undergone subtle manipulations that go unnoticed to the human eye while making it a totally different sight to the digital eye of a  machine learning… This story continues at The Next Web
imagealgorithmthatrighthumandetectionenoughoddly“gibbongibbon

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