Most existing deep neural networks (DNNs) are easily disturbed by slight noise. As far as we know, there are few researches on physical adversarial attack technology by deploying lighting equipment. The light-based physical adversarial attack technology has excellent covertness, which brings great security risks to many applications based on deep neural networks (such as automatic driving technology). Therefore, we propose a robust physical adversarial attack technology with excellent covertness, called adversarial laser point (AdvLS), which optimizes the physical parameters of laser point through genetic algorithm to perform physical adversarial attack. It realizes robust and covert physical adversarial attack by using low-cost laser equipment. As far as we know, AdvLS is the first light-based adversarial attack technology that can perform physical adversarial attacks in the daytime. A large number of experiments in the digital and physical environments show that AdvLS has excellent robustness and concealment. In addition, through in-depth analysis of the experimental data, we find that the adversarial perturbations generated by AdvLS have superior adversarial attack migration. The experimental results show that AdvLS impose serious interference to the advanced deep neural networks, we call for the attention of the proposed physical adversarial attack technology.

提出低成本的物理对抗攻击技术——Adversarial Laser Point，该技术利用遗传算法的方式优化激光点的物理参数来对深度神经网络进行对抗攻击，具有出色的隐蔽性和强鲁棒性。实验表明，该技术在数字和物理环境中均表现出极高的对抗干扰能力，同时生成的对抗扰动具有超越性的迁移攻击能力。

对抗性激光点攻击: 用于对DNNs的强大且隐秘的物理对抗攻击