mlsanityData to 14 Oct 2026

LaVAN: Localized and Visible Adversarial Noise

arXiv 1801.02608 ↗First posted 8 Jan 2018cs.CV cs.LGcited by 113

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Abstract (arXiv)

Most works on adversarial examples for deep-learning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without covering any of the main object(s) in the image. We show that it is possible to generate localized adversarial noises that cover only 2% of the pixels in the image, none of them over the main object, and that are transferable across images and locations, and successfully fool a state-of-the-art Inception v3 model with very high success rates.

Key facts. Cited by 113 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jan 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 16; 2023: 29; 2024: 19; 2025: 19; 2026: 8.

113 papers.

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  1. 2023AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learningcited by 103
  2. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 93
  3. 2021Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detectioncited by 67
  4. 2021PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifiercited by 65
  5. 2021Certified Patch Robustness via Smoothed Vision Transformerscited by 45
  6. 2022Towards Practical Certifiable Patch Defense with Vision Transformercited by 44
  7. 2022Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attackscited by 42
  8. 2021Harnessing Perceptual Adversarial Patches for Crowd Countingcited by 39
  9. 2021Are Vision Transformers Robust to Patch Perturbations?cited by 39
  10. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  11. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  12. 2023Jedi: Entropy-based Localization and Removal of Adversarial Patchescited by 35
38 more
  1. 2024PAD: Patch-Agnostic Defense against Adversarial Patch Attackscited by 32
  2. 2022PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patchcited by 30
  3. 2021DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attackscited by 29
  4. 2021PatchGuard++: Efficient Provable Attack Detection against Adversarial Patchescited by 29
  5. 2022ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patchescited by 27
  6. 2022ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Maskingcited by 22
  7. 2025Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Modelscited by 22
  8. 2023Efficient Decision-based Black-box Patch Attacks on Video Recognitioncited by 21
  9. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 20
  10. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  11. 2021Generative Dynamic Patch Attackcited by 19
  12. 2023Query-Efficient Decision-based Black-Box Patch Attackcited by 19
  13. 2021Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devicescited by 18
  14. 2022REAP: A Large-Scale Realistic Adversarial Patch Benchmarkcited by 18
  15. 2022Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Surveycited by 17
  16. 2022Certified Defences Against Adversarial Patch Attacks on Semantic Segmentationcited by 16
  17. 2023DIFFender: Diffusion-Based Adversarial Defense against Patch Attackscited by 15
  18. 2022Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defensecited by 14
  19. 2022Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detectioncited by 13
  20. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  21. 2021PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testingcited by 12
  22. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  23. 2023PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defensescited by 11
  24. 2021A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Trackingcited by 9
  25. 2022Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspectivecited by 8
  26. 2023Benchmarking Robustness to Adversarial Image Obfuscationscited by 8
  27. 2023Improving Viewpoint Robustness for Visual Recognition via Adversarial Trainingcited by 7
  28. 2023LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfercited by 7
  29. 2021Verifying Quantized Neural Networks using SMT-Based Model Checkingcited by 6
  30. 2021Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attackscited by 6
  31. 2022Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detectioncited by 6
  32. 2023ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patchescited by 6
  33. 2024Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attackscited by 6
  34. 2024Learning Visual Prompts for Guiding the Attention of Vision Transformerscited by 6
  35. 2022ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraintscited by 5
  36. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  37. 2024I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectorscited by 5
  38. 2024Real-world Adversarial Defense against Patch Attacks based on Diffusion Modelcited by 5

50 most cited of 113 All 113 (JSON)