Adversarial Attacks and Defense on Textual Data: A Review
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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 others. 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 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, 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.
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