Chapter 13: Adversarial Robustness
Machine learning interpretation has many concerns, ranging from knowledge discovery to high-stakes ones with tangible ethical implications, such as the fairness issues examined in the last two chapters. In this chapter, we will direct our attention to concerns involving reliability, safety, and security.
As we realized using the contrastive explanation method (CEM) in Chapter 8, Visualizing Convolutional Neural Networks, we can easily trick an image classifier into making embarrassingly false predictions. This ability can have serious ramifications. For instance, a perpetrator can place a black sticker on a yield sign, and while most drivers would still recognize this as a yield sign, a self-driving car would no longer recognize it and, as a result, crash. A bank robber could wear a cooling suit designed to trick a bank vault's thermal imaging system, and while any human would notice it, the imaging system wouldn't.
It doesn't...
