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.

我们研究了MDE系统对恶意补丁的脆弱性，提出了一种新颖的对MDE系统的隐蔽性恶意攻击方法（SAAM），通过破坏估计的距离或让物体与周围环境融为一体来损害MDE系统。我们的实验证明，设计的隐蔽补丁成功导致基于深度学习神经网络的MDE系统对物体深度估计出现显著的60%误差，在受影响区域的比例达到99%。而且，尽管是恶意攻击，补丁的外观自然，对人类观察者来说不易察觉。我们认为这项工作揭示了MDE系统在边缘设备上面临的恶意攻击威胁。希望引起社区的关注，意识到此类攻击可能造成的实际危害，并鼓励进一步研究以开发更强大和自适应的防御机制。

SAAM：对单目深度估计的隐蔽对抗攻击