Machine learning models are powerful but fallible. Generating adversarial
examples - inputs deliberately crafted to cause model misclassification or
other errors - can yield important insight into model assumptions and
vulnerabilities. Despite significant recent work on adversarial example
generation targeting image classifiers, relatively little work exists exploring
adversarial example generation for text classifiers; additionally, many
existing adversarial example generation algorithms require full access to
target model parameters, rendering them impractical for many real-world
attacks. In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm
for adversarial text example generation targeting largely black-box text
classifiers. We recast adversarial text example generation as a reinforcement
learning problem, and demonstrate that our algorithm offers preliminary but
promising steps towards generating semantically meaningful adversarial text
examples in a real-world attack scenario.

本研究提出 DANCin SEQ2SEQ，一种基于强化学习的算法来生成文本领域黑盒分类器的对抗样本，旨在探究模型的假设与漏洞。实验表明该算法能够初步但有前途地生成有意义的对抗性文本样本。

DANCin SEQ2SEQ: 使用对抗文本样本生成欺骗文本分类器

DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example  Generation

The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and tested on a newswire text categorization task. This method, which
we call uncertainty sampling, reduced by as much as 500-fold the amount of
training data that would have to be manually classified to achieve a given
level of effectiveness.

通过未分类数据顺序抽样来训练文本分类器可以大大降低所需的训练数据量，提高训练效率。