Adversarial Machine Learning in Text Analysis and Generation

TL;DR

This paper reviews recent research trends in adversarial machine learning applied to text analysis and generation, highlighting key algorithms, attack types, and defense strategies to improve model robustness.

Contribution

It provides a comprehensive summary of current research trends, including GAN models, attack methods, and defense mechanisms in adversarial text machine learning.

Findings

GAN algorithms and models are prominent in adversarial text ML.

Various attack types threaten the robustness of text models.

Defense strategies are evolving to counter sophisticated adversarial attacks.

Abstract

The research field of adversarial machine learning witnessed a significant interest in the last few years. A machine learner or model is secure if it can deliver main objectives with acceptable accuracy, efficiency, etc. while at the same time, it can resist different types and/or attempts of adversarial attacks. This paper focuses on studying aspects and research trends in adversarial machine learning specifically in text analysis and generation. The paper summarizes main research trends in the field such as GAN algorithms, models, types of attacks, and defense against those attacks.