Original paper

Universal Adversarial Attacks on Text Classifiers

Published: May 1, 2019
Abstract
Despite the vast success neural networks have achieved in different application domains, they have been proven to be vulnerable to adversarial perturbations (small changes in the input), which lead them to produce the wrong output. In this paper, we propose a novel method, based on gradient projection, for generating universal adversarial perturbations for text; namely sequence of words that can be added to any input in order to fool the...
Paper Details
Title
Universal Adversarial Attacks on Text Classifiers
Published Date
May 1, 2019
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