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.

中文翻译：

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文本分析和生成中的对抗机器学习

在过去的几年中，对抗性机器学习的研究领域引起了人们的极大兴趣。如果机器学习器或模型可以以可接受的准确性，效率等交付主要目标，同时又可以抵抗不同类型和/或对抗攻击的尝试，则它是安全的。本文重点研究对抗性机器学习的各个方面和研究趋势，尤其是在文本分析和生成方面。本文总结了该领域的主要研究趋势，例如GAN算法，模型，攻击类型以及针对这些攻击的防御。