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.

本文介绍了一种针对自然语言处理分类器的黑盒硬标签攻击的新方法，其中没有模型信息被公开，攻击者只能查询模型以获得分类器的最终决策，该攻击场景适用于用于情感分析和有毒内容检测等安全敏感应用的真实世界黑盒模型。

TextDecepter：针对文本分类器的硬标签黑盒攻击

TextDecepter: Hard Label Black Box Attack on Text Classifiers

With the development of high computational devices, deep neural networks
(DNNs), in recent years, have gained significant popularity in many Artificial
Intelligence (AI) applications. However, previous efforts have shown that DNNs
were vulnerable to strategically modified samples, named adversarial examples.
These samples are generated with some imperceptible perturbations but can fool
the DNNs to give false predictions. Inspired by the popularity of generating
adversarial examples for image DNNs, research efforts on attacking DNNs for
textual applications emerges in recent years. However, existing perturbation
methods for images cannotbe directly applied to texts as text data is discrete.
In this article, we review research works that address this difference and
generatetextual adversarial examples on DNNs. We collect, select, summarize,
discuss and analyze these works in a comprehensive way andcover all the related
information to make the article self-contained. Finally, drawing on the
reviewed literature, we provide further discussions and suggestions on this
topic.

该研究综述了最近针对文本深度神经网络攻击的研究。通过研究，讨论了现有攻击对文本数据不可直接应用的问题，并提出了关于这个话题的建议。