  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.
