  Adversarial samples are strategically modified samples, which are crafted
with the purpose of fooling a classifier at hand. An attacker introduces
specially crafted adversarial samples to a deployed classifier, which are being
mis-classified by the classifier. However, the samples are perceived to be
drawn from entirely different classes and thus it becomes hard to detect the
adversarial samples. Most of the prior works have been focused on synthesizing
adversarial samples in the image domain. In this paper, we propose a new method
of crafting adversarial text samples by modification of the original samples.
Modifications of the original text samples are done by deleting or replacing
the important or salient words in the text or by introducing new words in the
text sample. Our algorithm works best for the datasets which have
sub-categories within each of the classes of examples. While crafting
adversarial samples, one of the key constraint is to generate meaningful
sentences which can at pass off as legitimate from language (English)
viewpoint. Experimental results on IMDB movie review dataset for sentiment
analysis and Twitter dataset for gender detection show the efficiency of our
proposed method.
