  Adversarial examples are inputs to a machine learning system that result in
an incorrect output from that system. Attacks launched through this type of
input can cause severe consequences: for example, in the field of image
recognition, a stop signal can be misclassified as a speed limit
indication.However, adversarial examples also represent the fuel for a flurry
of research directions in different domains and applications. Here, we give an
overview of how they can be profitably exploited as powerful tools to build
stronger learning models, capable of better-withstanding attacks, for two
crucial tasks: fake news and social bot detection.
