  With the development of high computational devices, deep neural networks
(DNNs), in recent years, have gained significant popularity in many Artificial
Intelligence (AI) applications. However, previous efforts have shown that DNNs
were vulnerable to strategically modified samples, named adversarial examples.
These samples are generated with some imperceptible perturbations but can fool
the DNNs to give false predictions. Inspired by the popularity of generating
adversarial examples for image DNNs, research efforts on attacking DNNs for
textual applications emerges in recent years. However, existing perturbation
methods for images cannotbe directly applied to texts as text data is discrete.
In this article, we review research works that address this difference and
generatetextual adversarial examples on DNNs. We collect, select, summarize,
discuss and analyze these works in a comprehensive way andcover all the related
information to make the article self-contained. Finally, drawing on the
reviewed literature, we provide further discussions and suggestions on this
topic.
