Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a trained classifier. 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. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from the language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficacy of our proposed method.
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