mlsanityData to 7 Oct 2026

Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors

arXiv 2312.06163 ↗First posted 11 Dec 2023cs.CVcited by 0

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Abstract (arXiv)

Nowadays, the susceptibility of deep neural networks (DNNs) has garnered significant attention. Researchers are exploring patch-based physical attacks, yet traditional approaches, while effective, often result in conspicuous patches covering target objects. This leads to easy detection by human observers. Recently, novel camera-based physical attacks have emerged, leveraging camera patches to execute stealthy attacks. These methods circumvent target object modifications by introducing perturbations directly to the camera lens, achieving a notable breakthrough in stealthiness. However, prevailing camera-based strategies necessitate the deployment of multiple patches on the camera lens, which introduces complexity. To address this issue, we propose an Adversarial Camera Patch (ADCP).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 11 Dec 2023.

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27 of 39 references.

References · 27

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  6. 2017One pixel attack for fooling deep neural networkscited by 579
  7. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  8. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  9. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  10. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  11. 2021Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical Worldcited by 127
  12. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
15 more
  1. 2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
  2. 2019ColorFool: Semantic Adversarial Colorizationcited by 89
  3. 2018Semantic Adversarial Examplescited by 86
  4. 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
  5. 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
  6. 2019Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distancecited by 72
  7. 2019Adversarial camera stickers: A physical camera-based attack on deep learning systemscited by 69
  8. 2017Adversarial Attacks Beyond the Image Spacecited by 67
  9. 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
  10. 2022DTA: Physical Camouflage Attacks using Differentiable Transformation Networkcited by 51
  11. 2021Optical Adversarial Attackcited by 43
  12. 2022Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNscited by 11
  13. 2020Adversarial Rain Attack and Defensive Deraining for DNN Perceptioncited by 10
  14. 2023AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systemscited by 8
  15. 2023Adversarial Attack with Raindropscited by 7

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