Machine learning has been proven to be susceptible to carefully crafted samples, known as adversarial examples. The generation of these adversarial examples helps to make the models more robust and give as an insight of the underlying decision making of these models. Over the years, researchers have successfully attacked image classifiers in , both, white and black-box setting. Although , these methods are not directly applicable to texts as text data is discrete in nature . In recent years, research on crafting adversarial examples against textual applications has been on the rise. 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. Such attack scenario is applicable to real world black-box models being used for security-sensitive applications such as sentiment analysis and toxic content detection 
Machine learning has been proven to be susceptible to carefully crafted samples, known as adversarial examples. The generation of these adversarial examples helps to make the models more robust and gives us an insight into the underlying decision-making of these models. Over the years, researchers have successfully attacked image classifiers in both, white and black-box settings. However , these methods are not directly applicable to texts as text data is discrete . In recent years, research on crafting adversarial examples against textual applications has been on the rise. 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 a final decision of the classifier, without confidence scores of the classes involved. Such an attack scenario applies to real-world black-box models being used for security-sensitive applications such as sentiment analysis and toxic content detection .
R
give as an insight of the underlying decision making
gives us an insight into the underlying decision-making
D
,
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setting. Although
settings. However
D
in nature
R
hard label
hard-label
A
a
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attack scenario is applicable to real world
an attack scenario applies to real-world
A
.