mlsanityData to 14 Oct 2026

Optical Lens Attack on Deep Learning Based Monocular Depth Estimation

arXiv 2409.17376 ↗First posted 25 Sep 2024cs.CR cs.CVcited by 3

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Abstract (arXiv)

Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce LensAttack, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. LensAttack encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We begin by constructing a mathematical model of our attack, incorporating various attack parameters. Subsequently, we simulate the attack and evaluate its real-world performance in driving scenarios to demonstrate its effect on state-of-the-art MDE models. The results highlight the significant impact of LensAttack on the accuracy of depth estimation in AD systems.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 51 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

33 of 51 references.

References · 33

  1. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  2. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  3. 2018Pyramid Stereo Matching Networkcited by 489
  4. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  5. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  6. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  7. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 368
  8. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  9. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 234
  10. 2016Single-Image Depth Perception in the Wildcited by 180
  11. 2018Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videoscited by 155
  12. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
21 more
  1. 2019Self-Supervised Monocular Depth Hintscited by 127
  2. 2017Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenescited by 122
  3. 2014Deep Convolutional Neural Fields for Depth Estimation from a Single Imagecited by 106
  4. 2020Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systemscited by 100
  5. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  6. 2019How do neural networks see depth in single images?cited by 82
  7. 2018SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimationcited by 64
  8. 2020Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attackcited by 64
  9. 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patchescited by 57
  10. 2019AANet: Attribute Attention Network for Person Re-Identificationscited by 56
  11. 2020Targeted Adversarial Perturbations for Monocular Depth Predictioncited by 40
  12. 2020Adversarial Patch Attacks on Monocular Depth Estimation Networkscited by 29
  13. 2020Adversarial Attacks on Monocular Depth Estimationcited by 20
  14. 2023Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithmscited by 19
  15. 2017Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudescited by 15
  16. 2022Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognitioncited by 15
  17. 2023Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terraincited by 11
  18. 2021DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systemscited by 10
  19. 2020GhostImage: Remote Perception Attacks against Camera-based Image Classification Systemscited by 5
  20. 2018Realistic Image Degradation with Measured PSFcited by 4
  21. 2020Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimizationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Countingcited by 1
  2. 2025Can You Trust What You See? Alpha Channel No-Box Attacks on Video Object Detectioncited by 0
  3. 2026Scratched Lenses, Shifted Depth: Passive Camera-Side Optical Attackscited by 0