Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine
learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the
superior performance of DNNs in these applications, it has been recently shown that these
models are susceptible to a particular type of attack that exploits a fundamental flaw in their
design. This attack consists of generating particular synthetic examples referred to as
adversarial samples. These samples are constructed by slightly manipulating real data …