• Corpus ID: 214612137

Adversarial Attacks on Monocular Depth Estimation

@article{Zhang2020AdversarialAO,
  title={Adversarial Attacks on Monocular Depth Estimation},
  author={Ziqi Zhang and Xinge Zhu and Yingwei Li and Xiangqun Chen and Yao Guo},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.10315}
}
Recent advances of deep learning have brought exceptional performance on many computer vision tasks such as semantic segmentation and depth estimation. However, the vulnerability of deep neural networks towards adversarial examples have caused grave concerns for real-world deployment. In this paper, we present to the best of our knowledge the first systematic study of adversarial attacks on monocular depth estimation, an important task of 3D scene understanding in scenarios such as autonomous… 
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Methods Citations
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This work proposes the first defense mechanism against adversarial patches for a regression network, in the context of Monocular Depth Estimation on an edge device, maintaining performance on clean images while also achieving near clean image levels of performance on adversarial inputs.
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TLDR
The robustness of the most state-of-the-art monocular depth estimation networks against adversarial attacks is investigated and a novel deep feature annihilation loss is introduced that corrupts the hidden feature space representation forcing the decoder of the network to output poor depth maps.
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Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization
TLDR
An adversarial attack method to deep neural networks (DNNs) for monocular depth estimation, i.e., estimating the depth from a single image, that succeeds in attacking two DNN-based methods trained with indoor and outdoor scenes.
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
TLDR
This article investigates the impact of two primary types of adversarial attacks, perturbation attacks, and patch attacks, on the driving safety of vision-based autonomous vehicles rather than the detection precision of deep learning models and proposes an end-to-end evaluation framework with a set of driving safety performance metrics.
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TLDR
It is shown that a simple universal perturbation can fool a series of state-of-the-art defenses, and it is verified that regionally homogeneous perturbations can well transfer across different vision tasks.
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TLDR
This work proposes a method to improve the transferability of black-box adversarial attacks using lifelong learning, and uses the atrous Convolutional Neural Network model, which enables multi-task sequential learning, which saves more memory space than conventional multi- task learning.

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