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.

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