2021
DOI: 10.3390/app11209570
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CRank: Reusable Word Importance Ranking for Text Adversarial Attack

Abstract: Deep learning models have been widely used in natural language processing tasks, yet researchers have recently proposed several methods to fool the state-of-the-art neural network models. Among these methods, word importance ranking is an essential part that generates text adversarial examples, but suffers from low efficiency for practical attacks. To address this issue, we aim to improve the efficiency of word importance ranking, making steps towards realistic text adversarial attacks. In this paper, we propo… Show more

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References 37 publications
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