mlsanityData to 14 Oct 2026

Context-Aware Transfer Attacks for Object Detection

arXiv 2112.03223 ↗First posted 6 Dec 2021cs.CV cs.AI cs.LGcited by 18

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Abstract (arXiv)

Blackbox transfer attacks for image classifiers have been extensively studied in recent years. In contrast, little progress has been made on transfer attacks for object detectors. Object detectors take a holistic view of the image and the detection of one object (or lack thereof) often depends on other objects in the scene. This makes such detectors inherently context-aware and adversarial attacks in this space are more challenging than those targeting image classifiers. In this paper, we present a new approach to generate context-aware attacks for object detectors. We show that by using co-occurrence of objects and their relative locations and sizes as context information, we can successfully generate targeted mis-categorization attacks that achieve higher transfer success rates on blackbox object detectors than the state-of-the-art. We test our approach on a variety of object detectors with images from PASCAL VOC and MS COCO datasets and demonstrate up to 20 percentage points improvement in performance compared to the other state-of-the-art methods.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 37 of its 55 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

37 of 55 references.

References · 37

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2020End-to-End Object Detection with Transformerscited by 7,414
  4. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  6. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  7. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  8. 2018YOLOv3: An Incremental Improvementcited by 2,659
  9. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
  10. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  11. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  12. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
25 more
  1. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  2. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 536
  3. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 462
  4. 2019Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attackscited by 355
  5. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 333
  6. 2017Adversarial Examples for Semantic Segmentation and Object Detectioncited by 275
  7. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  8. 2019Simple Black-box Adversarial Attackscited by 251
  9. 2021Enhancing the Transferability of Adversarial Attacks through Variance Tuningcited by 244
  10. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
  11. 2018DPatch: An Adversarial Patch Attack on Object Detectorscited by 147
  12. 2019Improving Black-box Adversarial Attacks with a Transfer-based Priorcited by 121
  13. 2018Transferable Adversarial Attacks for Image and Video Object Detectioncited by 109
  14. 2020QEBA: Query-Efficient Boundary-Based Blackbox Attackcited by 104
  15. 2019FreeAnchor: Learning to Match Anchors for Visual Object Detectioncited by 90
  16. 2015Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networkscited by 80
  17. 2019Black-Box Adversarial Attack with Transferable Model-based Embeddingcited by 57
  18. 2020Robust Object Detection under Occlusion with Context-Aware CompositionalNetscited by 54
  19. 2018Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationshipscited by 41
  20. 2019Role of Spatial Context in Adversarial Robustness for Object Detectioncited by 35
  21. 2018Robust Adversarial Perturbation on Deep Proposal-based Modelscited by 26
  22. 2020Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistencycited by 16
  23. 2021Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenescited by 12
  24. 2017Exploring the Bounds of the Utility of Context for Object Detectioncited by 10
  25. 2021Towards Balanced Learning for Instance Recognitioncited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 3; 2025: 4; 2026: 1.

18 papers.

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  1. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 67
  2. 2023A Survey on Transferability of Adversarial Examples across Deep Neural Networkscited by 44
  3. 2022HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Designcited by 27
  4. 2022Zero-Query Transfer Attacks on Context-Aware Object Detectorscited by 17
  5. 2023Ensemble-based Blackbox Attacks on Dense Predictioncited by 16
  6. 2022Blackbox Attacks via Surrogate Ensemble Searchcited by 13
  7. 2022GAMA: Generative Adversarial Multi-Object Scene Attackscited by 13
  8. 2023Overload: Latency Attacks on Object Detection for Edge Devicescited by 13
  9. 2024A Survey and Evaluation of Adversarial Attacks for Object Detectioncited by 7
  10. 2025Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approachcited by 5
  11. 2025Exploring Adversarial Transferability between Kolmogorov-arnold Networkscited by 4
  12. 2024Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attackscited by 2
6 more
  1. 2023GLOW: Global Layout Aware Attacks on Object Detectioncited by 1
  2. 2025One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Modelscited by 1
  3. 2025Out-of-the-box: Black-box Causal Attacks on Object Detectorscited by 1
  4. 2022Object-fabrication Targeted Attack for Object Detectioncited by 0
  5. 2024On Feasibility of Intent Obfuscating Attackscited by 0
  6. 2026A Unified Spatial Alignment Framework for Highly Transferable Transformation-Based Attacks on Spatially Structured Taskscited by 0