  In the physical world, deep neural networks (DNNs) are impacted by light and
shadow, which can have a significant effect on their performance. While
stickers have traditionally been used as perturbations in most physical
attacks, their perturbations can often be easily detected. To address this,
some studies have explored the use of light-based perturbations, such as lasers
or projectors, to generate more subtle perturbations, which are artificial
rather than natural. In this study, we introduce a novel light-based attack
called the adversarial neon beam (AdvNB), which utilizes common neon beams to
create a natural black-box physical attack. Our approach is evaluated on three
key criteria: effectiveness, stealthiness, and robustness. Quantitative results
obtained in simulated environments demonstrate the effectiveness of the
proposed method, and in physical scenarios, we achieve an attack success rate
of 81.82%, surpassing the baseline. By using common neon beams as
perturbations, we enhance the stealthiness of the proposed attack, enabling
physical samples to appear more natural. Moreover, we validate the robustness
of our approach by successfully attacking advanced DNNs with a success rate of
over 75% in all cases. We also discuss defense strategies against the AdvNB
attack and put forward other light-based physical attacks.
