mlsanityData to 14 Oct 2026

Brightness-Restricted Adversarial Attack Patch

arXiv 2307.00421 ↗First posted 1 Jul 2023cs.CVcited by 1

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Abstract (arXiv)

Adversarial attack patches have gained increasing attention due to their practical applicability in physical-world scenarios. However, the bright colors used in attack patches represent a significant drawback, as they can be easily identified by human observers. Moreover, even though these attacks have been highly successful in deceiving target networks, which specific features of the attack patch contribute to its success are still unknown. Our paper introduces a brightness-restricted patch (BrPatch) that uses optical characteristics to effectively reduce conspicuousness while preserving image independence. We also conducted an analysis of the impact of various image features (such as color, texture, noise, and size) on the effectiveness of an attack patch in physical-world deployment. Our experiments show that attack patches exhibit strong redundancy to brightness and are resistant to color transfer and noise. Based on our findings, we propose some additional methods to further reduce the conspicuousness of BrPatch. Our findings also explain the robustness of attack patches observed in physical-world scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 1 Jul 2023.

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16 of 24 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2017Mask R-CNNcited by 7,277
  5. 2013Intriguing properties of neural networkscited by 5,155
  6. 2018YOLOv3: An Incremental Improvementcited by 2,659
  7. 2016Adversarial examples in the physical worldcited by 1,769
  8. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  9. 2017Adversarial Patchcited by 517
  10. 2018Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionalitycited by 282
  11. 2019Efficient Decision-based Black-box Adversarial Attacks on Face Recognitioncited by 175
  12. 2020Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNetscited by 167
4 more
  1. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  2. 2021On the Convergence and Robustness of Adversarial Trainingcited by 130
  3. 2020Adversarial Camouflage: Hiding Physical-World Attacks with Natural Stylescited by 126
  4. 2017Residual Convolutional CTC Networks for Automatic Speech Recognitioncited by 21

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