  Deep learning models have been used widely for various purposes in recent
years in object recognition, self-driving cars, face recognition, speech
recognition, sentiment analysis, and many others. However, in recent years it
has been shown that these models possess weakness to noises which force the
model to misclassify. This issue has been studied profoundly in the image and
audio domain. Very little has been studied on this issue concerning textual
data. Even less survey on this topic has been performed to understand different
types of attacks and defense techniques. In this manuscript, we accumulated and
analyzed different attacking techniques and various defense models to provide a
more comprehensive idea. Later we point out some of the interesting findings of
all papers and challenges that need to be overcome to move forward in this
field.
