Deep leaning 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 oth-
ers. However, in recent years it has been shown that these models
possess weakness to noises which forces the model to misclassify.
This issue has been studied profoundly in image and audio domain.
Very little has been studied on this issue with respect to textu-
al data. Even less survey on this topic has been performed to un-
derstand different types of attacks and defense techniques. In
this manuscript we accumulated and analyzed different attacking
techniques, various defense models on how to overcome this issue
in order 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 in order to move forward in this field.
