TextDecepter: Hard Label Black Box Attack on Text Classifiers
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Machine learning has been proven to be susceptible to carefully crafted samples, known as adversarialexamples. 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
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