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.

翻译：深度学习赋予了我们在复杂数据上训练高性能神经网络的能力。然而，随着研究的深入，神经网络的若干脆弱性逐渐暴露。对抗机器学习作为研究分支，旨在探索并理解导致神经网络对近似原始输入产生误分类的某些脆弱性。研究者提出了一类称为对抗攻击的算法，使神经网络在不同领域的各类任务中产生误分类。鉴于对抗攻击研究的广泛增长，理解其分类至关重要。这有助于系统化地认知脆弱性，并协助缓解对抗攻击的影响。本文从不同视角综述了现有对抗攻击及其理解，简要概述了现有对抗防御手段及其在减轻对抗攻击效果方面的局限性，并进一步讨论了对抗机器学习领域的未来研究方向。