论文标题
基于突变的文本生成，用于对抗机器学习应用
A Mutation-based Text Generation for Adversarial Machine Learning Applications
论文作者
论文摘要
许多自然语言相关的应用程序涉及人类或机器创建的文本生成。尽管在许多应用程序中，机器都支持人类，但在其他几个应用程序中（例如，对抗机器学习，社交机器人和巨魔）机器试图模仿人类。在此范围内，我们提出并评估了几种基于突变的文本生成方法。与基于机器的生成文本不同，基于突变的生成文本需要人类文本样本作为输入。我们展示了突变操作员的示例，但是这项工作可以在许多方面扩展，例如根据应用程序的性质提出新的基于文本的突变操作员。
Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines try to impersonate humans. In this scope, we proposed and evaluated several mutation-based text generation approaches. Unlike machine-based generated text, mutation-based generated text needs human text samples as inputs. We showed examples of mutation operators but this work can be extended in many aspects such as proposing new text-based mutation operators based on the nature of the application.
