A Mutation-based Text Generation for Adversarial Machine Learning Applications
This work addresses the need for text generation in adversarial applications like social bots, but it appears incremental as it builds on existing mutation concepts without major breakthroughs.
The paper tackled the problem of generating text for adversarial machine learning by proposing mutation-based approaches that require human text samples as inputs, and it evaluated several methods without specifying concrete numerical results.
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.