In this paper, we investigate the vulnerability of MDE to adversarial patches. We propose a novel \underline{S}tealthy \underline{A}dversarial \underline{A}ttacks on \underline{M}DE (SAAM) that compromises MDE by either corrupting the estimated distance or causing an object to seamlessly blend into its surroundings. Our experiments, demonstrate that the designed stealthy patch successfully causes a DNN-based MDE to misestimate the depth of objects. In fact, our proposed adversarial patch achieves a significant 60\% depth error with 99\% ratio of the affected region. Importantly, despite its adversarial nature, the patch maintains a naturalistic appearance, making it inconspicuous to human observers. We believe that this work sheds light on the threat of adversarial attacks in the context of MDE on edge devices. We hope it raises awareness within the community about the potential real-life harm of such attacks and encourages further research into developing more robust and adaptive defense mechanisms.

翻译：本文研究了单目深度估计（MDE）模型对对抗性补丁的脆弱性。我们提出了一种新颖的隐蔽式对抗攻击方法（SAAM），通过破坏估计距离或使目标物体无缝融入背景，从而削弱MDE模型性能。实验表明，所设计的隐蔽式补丁能成功误导基于深度神经网络的MDE模型错误估计物体深度。具体而言，我们的对抗性补丁可使受影响区域占比达99%的情况下实现60%的深度误差。重要的是，尽管具有对抗性，该补丁仍保持自然外观，使人类观察者难以察觉。我们相信，这项工作揭示了边缘设备上MDE模型面临的对抗攻击威胁，希望引起学界对这类攻击潜在现实危害的重视，并推动更鲁棒和自适应的防御机制研究。