Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. Yolo v6 demonstrates remarkable resilience, experiencing just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its ``limitation''.In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks.

翻译：对抗攻击可能危及真实世界检测模型的鲁棒性。然而，由于资源密集型实验的挑战，在真实世界条件下评估这些模型存在困难。虚拟仿真提供了一种替代方案，但缺乏标准化基准阻碍了进展。针对这一问题，我们提出了一种创新的实例级数据生成流程，使用CARLA模拟器。通过该流程，我们建立了离散与连续实例级（DCI）数据集，实现了对三种检测模型和三种物理对抗攻击的全面实验。我们的发现揭示了模型在对抗条件下的多样化表现。Yolo v6展现出显著弹性，平均精度（AP）仅下降6.59%。相比之下，ASA攻击导致AP平均下降14.51%，效果是其他算法的两倍。我们还注意到，静态场景产生更高的识别AP值，且在不同天气条件下结果保持相对一致。有趣的是，我们的研究表明，对抗攻击算法的进步可能正接近其“极限”。总之，我们的工作强调了对抗攻击在真实世界背景下的重要性，并引入了DCI数据集作为通用基准。我们的发现为增强检测模型的鲁棒性提供了宝贵见解，并为对抗攻击领域的未来研究提供了指导。