Adversarial Neon Beam: A Light-based Physical Attack to DNNs

Chengyin Hu, Weiwen Shi, Wen Li

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.

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