Deep neural networks (DNNs) remain vulnerable to adversarial attacks that cause misclassification when specific perturbations are added to input images. This vulnerability also threatens the reliability of DNN-based monocular depth estimation (MDE) models, making robustness enhancement a critical need in practical applications. To validate the vulnerability of DNN-based MDE models, this study proposes a projection-based adversarial attack method that projects perturbation light onto a target object. The proposed method employs physics-in-the-loop (PITL) optimization -- evaluating candidate solutions in actual environments to account for device specifications and disturbances -- and utilizes a distributed covariance matrix adaptation evolution strategy. Experiments confirmed that the proposed method successfully created adversarial examples that lead to depth misestimations, resulting in parts of objects disappearing from the target scene.

翻译：深度神经网络（DNNs）在输入图像中添加特定扰动时容易受到导致错误分类的对抗攻击。这种脆弱性同样威胁着基于DNN的单目深度估计（MDE）模型的可靠性，使得在实际应用中增强鲁棒性成为关键需求。为验证基于DNN的MDE模型的脆弱性，本研究提出一种基于投影的对抗攻击方法，该方法将扰动光投射到目标物体上。所提方法采用物理在环（PITL）优化——通过在真实环境中评估候选解来考虑设备规格和干扰——并利用分布式协方差矩阵自适应进化策略。实验证实，所提方法成功生成了导致深度估计错误的对抗样本，使得目标场景中物体的部分区域消失。