Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksDownload PDF

Published: 01 Feb 2023, 19:20, Last Modified: 19 Feb 2023, 04:34ICLR 2023 notable top 25%Readers: Everyone
Keywords: Adversarial Training, Monocular Depth Estimation, Adversarial Attack, Self-supervised Learning.
TL;DR: Use self-supervised adversarial training to harden monocular depth estimation models against physical-world adversarial attacks.
Abstract: Monocular Depth Estimation (MDE) is a critical component in applications such as autonomous driving. There are various attacks against MDE networks. These attacks, especially the physical ones, pose a great threat to the security of such systems. Traditional adversarial training method requires ground-truth labels and hence cannot be directly applied to self-supervised MDE that does not have depth ground truth. Some self-supervised model hardening technique (e.g., contrastive learning) ignores the domain knowledge of MDE and can hardly achieve optimal performance. In this work, we propose a novel adversarial training method for self-supervised MDE models based on view synthesis without using the depth ground truth. We improve adversarial robustness against physical-world attacks using $L_0$-norm-bounded perturbation in training. We compare our method with supervised learning-based and contrastive learning-based methods that are tailored for MDE. Results on two representative MDE networks show that we achieve better robustness against various adversarial attacks with nearly no benign performance degradation.
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