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.

深度学习使我们能够高效地训练复杂数据的神经网络。然而，随着研究的增长，神经网络的几个弱点也被曝光。对抗机器学习是一种特定的研究领域，旨在利用和理解导致神经网络因输入接近原始输入而错误分类的一些弱点。提出了一类称为对抗性攻击的算法，用于在不同领域的各种任务上使神经网络错误分类。随着对对抗性攻击的广泛研究，了解对抗性攻击的分类是至关重要的。这将有助于我们以系统性的方式了解弱点，并帮助我们减轻对抗性攻击的影响。本文对现有的对抗性攻击及其不同角度的理解进行了概述，并简要介绍了现有对抗性防御措施及其在减轻对抗性攻击效果方面的局限性。此外，我们讨论了对抗机器学习领域未来研究的方向。