mlsanityData to 7 Oct 2026

Adversarial Attack with Raindrops

arXiv 2302.14267 ↗First posted 28 Feb 2023cs.CVcited by 7

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Abstract (arXiv)

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples, which are usually designed artificially to fool DNNs, but rarely exist in real-world scenarios. In this paper, we study the adversarial examples caused by raindrops, to demonstrate that there exist plenty of natural phenomena being able to work as adversarial attackers to DNNs. Moreover, we present a new approach to generate adversarial raindrops, denoted as AdvRD, using the generative adversarial network (GAN) technique to simulate natural raindrops. The images crafted by our AdvRD look very similar to the real-world raindrop images, statistically close to the distribution of true raindrop images, and more importantly, can perform strong adversarial attack to the state-of-the-art DNN models. On the other side, we show that the adversarial training using our AdvRD images can significantly improve the robustness of DNNs to the real-world raindrop attacks. Extensive experiments are carried out to demonstrate that the images crafted by AdvRD are visually and statistically close to the natural raindrop images, can work as strong attackers to DNN models, and also help improve the robustness of DNNs to raindrop attacks.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

34 of 47 references.

References · 34

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22 more
  1. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  2. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  3. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  4. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 591
  5. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
  6. 2019Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attackscited by 440
  7. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 352
  8. 2018Adversarial Risk and the Dangers of Evaluating Against Weak Attackscited by 221
  9. 2017Attentive Generative Adversarial Network for Raindrop Removal from a Single Imagecited by 204
  10. 2018Physical Adversarial Examples for Object Detectorscited by 163
  11. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  12. 2018Adversarial Attacks and Defences Competitioncited by 96
  13. 2019NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networkscited by 94
  14. 2020On Success and Simplicity: A Second Look at Transferable Targeted Attackscited by 89
  15. 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
  16. 2019Adversarial camera stickers: A physical camera-based attack on deep learning systemscited by 69
  17. 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
  18. 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 49
  19. 2021Optical Adversarial Attackcited by 43
  20. 2020Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effectcited by 40
  21. 2016Learning a Driving Simulatorcited by 33
  22. 2021AdvHaze: Adversarial Haze Attackcited by 31

Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 2; 2025: 1.

7 papers.

  1. 2023Towards Unified Deep Image Deraining: A Survey and A New Benchmarkcited by 34
  2. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  3. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  4. 2024Cloud Adversarial Example Generation for Remote Sensing Image Classificationcited by 2
  5. 2024SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledgecited by 1
  6. 2023Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectorscited by 0
  7. 2025FeatureFool: Zero-Query Fooling of Video Models via Feature Mapcited by 0