Before revision:  In this paper, we present a novel approach for hard label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get  final decision of the classifier, without confidence scores of the classes involved.
After revision:  In this paper, we present a novel approach for hard-label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get final decision of the classifier, without confidence scores of the classes involved.