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 classifier makes a wrong prediction. Simple character-level transformations are applied to the highestranked words in order to minimize the edit distance of the perturbation. We evaluated DeepWordBug on two real-world text datasets: Enron spam emails and IMDB movie reviews. Our experimental results indicate that DeepWordBug can reduce the classification accuracy from 99% to 40% on Enron and from 87% to 26% on IMDB. Our results strongly demonstrate that the generated adversarial sequences from a deep-learning model can similarly evade other deep models. 50 2018 IEEE Symposium on Security and Privacy Workshops
For black box setting, REF develops score functions to find out the works to modify.
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
2018 IEEE Security and Privacy Workshops (SPW)
2018 IEEE Security and Privacy Workshops (SPW)
Computer Science