mlsanityData to 14 Oct 2026

Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection

arXiv 2209.13353 ↗First posted 27 Sep 2022cs.CVcited by 6

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Abstract (arXiv)

Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image. In this work, we perform an in-depth analysis of different patch generation parameters, including initialization, patch size, and especially positioning a patch in an image during training. We focus on the object vanishing attack and run experiments with YOLOv3 as a model under attack in a white-box setting and use images from the COCO dataset. Our experiments have shown, that inserting a patch inside a window of increasing size during training leads to a significant increase in attack strength compared to a fixed position. The best results were obtained when a patch was positioned randomly during training, while patch position additionally varied within a batch.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

16 of 22 references.

References · 16

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  4. 2013Intriguing properties of neural networkscited by 5,155
  5. 2018YOLOv3: An Incremental Improvementcited by 2,659
  6. 2016Universal adversarial perturbationscited by 784
  7. 2017Adversarial Patchcited by 517
  8. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  9. 2018Physical Adversarial Examples for Object Detectorscited by 163
  10. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  11. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  12. 2018DPatch: An Adversarial Patch Attack on Object Detectorscited by 147
4 more
  1. 2018LaVAN: Localized and Visible Adversarial Noisecited by 113
  2. 2019On Physical Adversarial Patches for Object Detectioncited by 93
  3. 2021Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attackscited by 55
  4. 2022Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detectioncited by 13

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 1.

6 papers.

  1. 2024Effectiveness Assessment of Recent Large Vision-Language Modelscited by 29
  2. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  3. 2024Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detectioncited by 11
  4. 2025Runtime Safety Monitoring of Deep Neural Networks for Perception: A Surveycited by 2
  5. 2024Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Modelscited by 0
  6. 2024Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselinescited by 0