多尺度与视角下的对抗样本：我们如何“骗过”神经网络分类器
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives.
This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.