Another AI attack, this time against 'black box' machine learning

Would you like to join the merry band of researchers breaking machine learning models? A trio of German researchers has published a tool designed to make it easier to craft adversarial models when you're attacking a "black box". Unlike adversarial models that attack AIs "from the inside", attacks developed for black boxes could be used against closed system like autonomous cars, security (facial recognition, for example), or speech recognition (Alexa or Cortana). The tool, called Foolbox, is currently under review for presentation at next year's International Conference on Learning Representations (kicking off at the end of April). Wieland Brendel, Jonas Rauber and Matthias Bethge of the Eberhard Karls University Tubingen, Germany explained at arXiv that Foolbox is a "decision-based" attack called a boundary attack which "starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial".