Adversarial Machine Learning in Text Analysis and Generation
It addresses security challenges in text-based AI systems, but is incremental as it synthesizes existing work without introducing new methods.
This paper reviews research trends in adversarial machine learning for text analysis and generation, summarizing key aspects like GAN algorithms, attack types, and defense strategies.
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.