Targeted Adversarial Perturbations for Monocular Depth Prediction
Targeted Adversarial Perturbations for Monocular Depth Prediction
复制标题
用于单目深度预测的有针对性的对抗性扰动
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Stefano Soatto
中科院分区：
文献类型：
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作者：
A. Wong;Safa Cicek;Stefano Soatto
我们研究了对抗扰动对单目深度预测任务的影响。具体来说，我们探索的能力，小，难以察觉的添加剂扰动选择性地改变感知的几何场景。我们表明，这种扰动不仅可以在全球范围内重新调整预测的距离相机，但也改变了预测，以匹配不同的目标场景。我们还表明，当给定语义或实例信息时，扰动可以欺骗网络来改变场景中特定类别或实例的深度，甚至在保留场景其余部分的同时删除它们。为了了解目标扰动的影响，我们对最先进的单目深度预测方法进行了实验。我们的实验揭示了单目深度预测网络的漏洞，并揭示了它们所学到的偏见和背景。
We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to selectively alter the perceived geometry of the scene. We show that such perturbations can not only globally re-scale the predicted distances from the camera, but also alter the prediction to match a different target scene. We also show that, when given semantic or instance information, perturbations can fool the network to alter the depth of specific categories or instances in the scene, and even remove them while preserving the rest of the scene. To understand the effect of targeted perturbations, we conduct experiments on state-of-the-art monocular depth prediction methods. Our experiments reveal vulnerabilities in monocular depth prediction networks, and shed light on the biases and context learned by them.
影响因子：
19.5
作者：
Abu Alhaija, Hassan;Mustikovela, Siva Karthik;Rother, Carsten
通讯作者：
Rother, Carsten