  Most existing deep neural networks (DNNs) are easily disturbed by slight
noise. However, there are few researches on physical attacks by deploying
lighting equipment. The light-based physical attacks has excellent covertness,
which brings great security risks to many vision-based applications (such as
self-driving). Therefore, we propose a light-based physical attack, called
adversarial laser spot (AdvLS), which optimizes the physical parameters of
laser spots through genetic algorithm to perform physical attacks. It realizes
robust and covert physical attack by using low-cost laser equipment. As far as
we know, AdvLS is the first light-based physical attack that perform physical
attacks in the daytime. A large number of experiments in the digital and
physical environments show that AdvLS has excellent robustness and covertness.
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 advanced DNNs, we call for the attention of the proposed AdvLS.
The code of AdvLS is available at: https://github.com/ChengYinHu/AdvLS
