Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks
<|reference_start|>Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks: Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant threats to system security. Traditional adversarial training methods, which require ground-truth labels, are not directly applicable to MDE models that lack ground-truth depth. Some self-supervised model hardening techniques (e.g., contrastive learning) overlook the domain knowledge of MDE, resulting in suboptimal performance. In this work, we introduce a novel self-supervised adversarial training approach for MDE models, leveraging view synthesis without the need for ground-truth depth. We enhance adversarial robustness against real-world attacks by incorporating L_0-norm-bounded perturbation during training. We evaluate our method against supervised learning-based and contrastive learning-based approaches specifically designed for MDE. Our experiments with two representative MDE networks demonstrate improved robustness against various adversarial attacks, with minimal impact on benign performance.<|reference_end|>
@article{cheng2024self-supervised,
    title={Self-supervised Adversarial Training of Monocular Depth Estimation
  against Physical-World Attacks},
    author={Zhiyuan Cheng, Cheng Han, James Liang, Qifan Wang, Xiangyu Zhang,
  Dongfang Liu},
    journal={arXiv preprint arXiv:2406.05857},
    year={2024},
    doi={10.1109/TPAMI.2024.3412632},
    archivePrefix={arXiv},
    eprint={2406.05857},
    primaryClass={cs.CV}
}