Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to the computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is being captured and converted to a binary image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. As the perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. They are highly invisibility and executable and can pose a significant or even lethal threats to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques. 中文翻译： 对抗性攻击可以通过对原始输入植入人眼无法察觉的微小扰动来误导深度学习模型做出错误预测，这对基于深度学习的计算机视觉系统构成了巨大的安全威胁。与数字对抗攻击相比，物理对抗攻击更真实，因为在输入被捕获并转换为视觉系统内的二进制图像之前，扰动被引入输入。在本文中，我们关注物理对抗性攻击，并将其进一步分为侵入性和非侵入性。基于光学的物理对抗攻击技术（例如使用光照射）属于非侵入性类别。由于扰动与现实世界中自然环境产生的影响非常相似，因此人类很容易忽略这些扰动。它们具有高度隐蔽性和可执行性，可以对真实系统构成重大甚至致命的威胁。本文主要研究基于光学的计算机视觉系统物理对抗攻击技术，重点介绍和讨论基于光学的物理对抗攻击技术。