The use of computer vision technologies to boost machine learning continues to accelerate, driven by optimism that classifying huge volumes of images will unleash all sorts of new applications and forms of autonomy. But there's a darker side to this transformation: These learning systems remain remarkably easy to fool using so-called "adversarial attacks." Even worse is that leading researchers acknowledge they don't really have a solution for stopping mischief makers from wreaking havoc on these systems. "Can we defend against these attacks?" said Nicolas Papernot, a research scientist at Google Brain, the company's deep learning artificial intelligence research team. "Unfortunately, the answer is no."