mlsanityData to 14 Oct 2026

Eigenpatches -- Adversarial Patches from Principal Components

arXiv 2306.10963 ↗First posted 19 Jun 2023cs.CVcited by 1

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Abstract (arXiv)

Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computational expensiveness by analyzing 375 generated patches, calculating the principal components of these and show, that linear combinations of the resulting "eigenpatches" can be used to fool object detections successfully.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 19 Jun 2023.

Builds on

19 of 29 references.

References · 19

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2020End-to-End Object Detection with Transformerscited by 7,414
  3. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  4. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  5. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
  6. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  7. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  8. 2017The Space of Transferable Adversarial Examplescited by 158
  9. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  10. 2018Are adversarial examples inevitable?cited by 113
  11. 2022Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real Worldcited by 42
  12. 2023Jedi: Entropy-based Localization and Removal of Adversarial Patchescited by 35
7 more
  1. 2023A Survey on the Robustness of Computer Vision Models against Common Corruptionscited by 21
  2. 2021Learning Coated Adversarial Camouflages for Object Detectorscited by 17
  3. 2023Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Featurecited by 6
  4. 2023How many dimensions are required to find an adversarial example?cited by 5
  5. 2022Origins of Low-dimensional Adversarial Perturbationscited by 4
  6. 2022A Tutorial on Adversarial Learning Attacks and Countermeasurescited by 3
  7. 2021Energy Attack: On Transferring Adversarial Examplescited by 2

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Traversing the Subspace of Adversarial Patchescited by 1