mlsanityData to 7 Oct 2026

Adversarial Patch Attacks on Monocular Depth Estimation Networks

arXiv 2010.03072 ↗First posted 6 Oct 2020cs.CV cs.CR eess.IVcited by 29

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Abstract (arXiv)

Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years. However, depth estimation from a monocular image alone is essentially an ill-posed problem, and thus, it seems that this approach would have inherent vulnerabilities. To reveal this limitation, we propose a method of adversarial patch attack on monocular depth estimation. More specifically, we generate artificial patterns (adversarial patches) that can fool the target methods into estimating an incorrect depth for the regions where the patterns are placed. Our method can be implemented in the real world by physically placing the printed patterns in real scenes. We also analyze the behavior of monocular depth estimation under attacks by visualizing the activation levels of the intermediate layers and the regions potentially affected by the adversarial attack.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 8; 2024: 6; 2025: 5; 2026: 2.

29 papers.

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  1. 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patchescited by 57
  2. 2021Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attackscited by 55
  3. 2024Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Drivingcited by 32
  4. 2022On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Drivingcited by 30
  5. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  6. 2022Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Surveycited by 17
  7. 2023SAAM: Stealthy Adversarial Attack on Monocular Depth Estimationcited by 12
  8. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  9. 2023APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigationcited by 10
  10. 2022CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Modelscited by 8
  11. 2023Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Contextcited by 8
  12. 2021LiDARTouch: Monocular metric depth estimation with a few-beam LiDARcited by 7
17 more
  1. 2022Physical Passive Patch Adversarial Attacks on Visual Odometry Systemscited by 6
  2. 2023Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flowcited by 6
  3. 2024SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applicationscited by 4
  4. 2023Distributional Modeling for Location-Aware Adversarial Patchescited by 3
  5. 2024Optical Lens Attack on Deep Learning Based Monocular Depth Estimationcited by 3
  6. 2023Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramidscited by 2
  7. 2025Task-Agnostic Attacks Against Vision Foundation Modelscited by 1
  8. 2025Adversarial Examples in Environment Perception for Automated Driving (Review)cited by 1
  9. 2025Endoscopic Depth Estimation Based on Deep Learning: A Surveycited by 1
  10. 2025Guided Diffusion-based Generation of Adversarial Objects for Real-World Monocular Depth Estimation Attackscited by 1
  11. 2026Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusioncited by 1
  12. 2023Masking and Mixing Adversarial Trainingcited by 0
  13. 2024Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patchescited by 0
  14. 2024Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attackcited by 0
  15. 2024Optical Lens Attack on Monocular Depth Estimation for Autonomous Drivingcited by 0
  16. 2025BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splattingcited by 0
  17. 2026Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policiescited by 0