mlsanityData to 14 Oct 2026

Adversarial Examples in Environment Perception for Automated Driving (Review)

arXiv 2504.08414 ↗First posted 11 Apr 2025cs.CVcited by 1

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Abstract (arXiv)

The renaissance of deep learning has led to the massive development of automated driving. However, deep neural networks are vulnerable to adversarial examples. The perturbations of adversarial examples are imperceptible to human eyes but can lead to the false predictions of neural networks. It poses a huge risk to artificial intelligence (AI) applications for automated driving. This survey systematically reviews the development of adversarial robustness research over the past decade, including the attack and defense methods and their applications in automated driving. The growth of automated driving pushes forward the realization of trustworthy AI applications. This review lists significant references in the research history of adversarial examples.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 202 of its 260 references that are in the map. First posted on arXiv 11 Apr 2025.

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202 of 260 references.

References · 202

  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. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  5. 2023Segment Anythingcited by 8,234
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2017Mask R-CNNcited by 7,277
  8. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  11. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  12. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
38 more
  1. 2014Going Deeper with Convolutionscited by 4,241
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  4. 2016Wide Residual Networkscited by 3,070
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  7. 2018YOLOv3: An Incremental Improvementcited by 2,659
  8. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  9. 2016Adversarial examples in the physical worldcited by 1,769
  10. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  11. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  12. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,451
  13. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  14. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  15. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  16. 2022Planning-oriented Autonomous Drivingcited by 1,087
  17. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,056
  18. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  19. 2016Adversarial Machine Learning at Scalecited by 936
  20. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  21. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  22. 2018Robustness May Be at Odds with Accuracycited by 880
  23. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  24. 2016Universal adversarial perturbationscited by 784
  25. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  26. 2020Fast is better than free: Revisiting adversarial trainingcited by 727
  27. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 685
  28. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  29. 2019Adversarial Training for Free!cited by 643
  30. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
  31. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 567
  32. 2020On Adaptive Attacks to Adversarial Example Defensescited by 555
  33. 2017Countering Adversarial Images using Input Transformationscited by 520
  34. 2017Adversarial Patchcited by 517
  35. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 501
  36. 2022Diffusion Models for Adversarial Purificationcited by 476
  37. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  38. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466

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  1. 2025LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detectioncited by 0