  Deep neural networks (DNNs) have achieved remarkable success in various tasks
(e.g., image classification, speech recognition, and natural language
processing (NLP)). However, researchers have demonstrated that DNN-based models
are vulnerable to adversarial examples, which cause erroneous predictions by
adding imperceptible perturbations into legitimate inputs. Recently, studies
have revealed adversarial examples in the text domain, which could effectively
evade various DNN-based text analyzers and further bring the threats of the
proliferation of disinformation. In this paper, we give a comprehensive survey
on the existing studies of adversarial techniques for generating adversarial
texts written by both English and Chinese characters and the corresponding
defense methods. More importantly, we hope that our work could inspire future
studies to develop more robust DNN-based text analyzers against known and
unknown adversarial techniques.
  We classify the existing adversarial techniques for crafting adversarial
texts based on the perturbation units, helping to better understand the
generation of adversarial texts and build robust models for defense. In
presenting the taxonomy of adversarial attacks and defenses in the text domain,
we introduce the adversarial techniques from the perspective of different NLP
tasks. Finally, we discuss the existing challenges of adversarial attacks and
defenses in texts and present the future research directions in this emerging
and challenging field.
