A reading survey on adversarial machine learning: Adversarial attacks
  and their understanding
Shashank Kotyan
Deep Learning has empowered us to train neural networks for complex data with
high performance. However, with the growing research, several vulnerabilities
in neural networks have been exposed. A particular branch of research,
Adversarial Machine Learning, exploits and understands some of the
vulnerabilities that cause the neural networks to misclassify for near original
input. A class of algorithms called adversarial attacks is proposed to make the
neural networks misclassify for various tasks in different domains. With the
extensive and growing research in adversarial attacks, it is crucial to
understand the classification of adversarial attacks. This will help us
understand the vulnerabilities in a systematic order and help us to mitigate
the effects of adversarial attacks. This article provides a survey of existing
adversarial attacks and their understanding based on different perspectives. We
also provide a brief overview of existing adversarial defences and their
limitations in mitigating the effect of adversarial attacks. Further, we
conclude with a discussion on the future research directions in the field of
adversarial machine learning.