mlsanityData to 14 Oct 2026

A Mutation-based Text Generation for Adversarial Machine Learning Applications

arXiv 2212.11808 ↗First posted 21 Dec 2022cs.CL cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 50 references that are in the map. First posted on arXiv 21 Dec 2022.

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24 of 50 references.

References · 24

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12 more
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