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Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
2018
2018 IEEE Security and Privacy Workshops (SPW)
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We develop novel scoring strategies to find the most important words to modify such that the deep
doi:10.1109/spw.2018.00016
dblp:conf/sp/GaoLSQ18
fatcat:cehdytzqhjhsneufwlja66ec5m