论文标题
对抗激光斑点：强大和秘密的物理世界攻击DNNS
Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs
论文作者
论文摘要
大多数现有的深神经网络（DNN）很容易被轻微的噪音打扰。但是，通过部署照明设备对身体攻击的研究很少。基于光的物理攻击具有出色的秘密性，这为许多基于视觉的应用（例如自动驾驶）带来了很大的安全风险。因此，我们提出了一种基于光的物理攻击，称为对抗激光点（ADVLS），该攻击通过遗传算法优化激光点的物理参数以执行物理攻击。它通过使用低成本激光设备实现了强大的和秘密的物理攻击。据我们所知，ADVLS是白天进行物理攻击的第一个基于轻的物理攻击。在数字和物理环境中进行的大量实验表明，ADVL具有出色的鲁棒性和秘密性。此外，通过对实验数据的深入分析，我们发现ADVL产生的对抗扰动具有优越的对抗攻击迁移。实验结果表明，ADVL对高级DNN施加了严重干扰，我们呼吁提出的ADVL的注意。 advls守则可在以下网址获得：https：//github.com/chengyinhu/advls
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
