One-Pixel Attack: Understanding and Improving Deep Neural Networks with Evolutionary Computation

Recently, the one-pixel attack showed that deep neural networks (DNNs) can misclassify by changing only one pixel. Beyond a vulnerability, by demonstrating how easy it is to cause a change in classes, it revealed that DNNs are not learning the expected high-level features but rather less robust ones. In this chapter, recent findings further confirming the affirmations above will be presented together with an overview of current attacks and defenses. Moreover, it will be shown the promises of evolutionary computation as both a way to investigate the robustness of DNNs as well as a way to improve their robustness through hybrid systems, evolution of architectures, among others.