Paper Citation Record · LEDGER

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems

As of 16 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2505.11532.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.11532 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:45:13.105399Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T17:08:58.831798Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

verified exact2
verified fuzzy36
unresolved19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 315b6922-0fa1-459a-9849-0829e92d10aa · outbound

This paper cites Autonomous driving system: A comprehensive survey,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Autonomous driving system: A comprehensive survey,

Reference 1

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Observation afdd6acd-c282-45a9-8381-204f7eccacc1 · outbound

This paper cites (2024) Model y owner’s manual.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems (2024) Model y owner’s manual

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.801945Z

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Observation 7a74a833-72f1-48ad-8ee9-7d7a44c90c2c · outbound

This paper cites Deep learning-based perception systems for autonomous driving: A comprehensive survey,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Deep learning-based perception systems for autonomous driving: A comprehensive survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.779890Z

Source-reported events for the cited work

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Observation 8e0245a3-e0c3-41c3-a862-d02be5ccd2f1 · outbound

This paper cites Adversarial sensor attack on lidar-based perception in autonomous driving,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial sensor attack on lidar-based perception in autonomous driving,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.768798Z

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Observation 99307ef8-6aed-4d64-b7a5-7c28e93cd9ac · outbound

This paper cites Adversarial attacks on autonomous driving systems in the physical world: a survey,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial attacks on autonomous driving systems in the physical world: a survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.757627Z

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Observation e272b560-fd27-45fd-a65b-e549241c7ea0 · outbound

This paper cites Adversarial driving: Attacking end-to-end autonomous driving,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial driving: Attacking end-to-end autonomous driving,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.746212Z

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Observation a3c4fde0-8fa2-4654-ba65-b0a46624c329 · outbound

This paper cites Available: https://www .tesla.com/ownersmanual/modely/ en_us/GUID-2CB60804-9CEA-4F4B-8B04-09B991368DC5 .html.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Available: https://www .tesla.com/ownersmanual/modely/ en_us/GUID-2CB60804-9CEA-4F4B-8B04-09B991368DC5 .html

Reference 7

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Observation 78173cf2-edec-41a9-a456-ec016b9eb294 · outbound

This paper cites [Online].

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems [Online]

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.734300Z

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Observation d303dbbc-2eea-4aaf-868a-db03b84efe48 · outbound

This paper cites OpenPilot.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems OpenPilot

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.721253Z

Source-reported events for the cited work

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Observation f69e24e9-bc01-4da1-931d-055a259857cd · outbound

This paper cites (2024) Gaussian noise - wikipedia.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems (2024) Gaussian noise - wikipedia

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.708773Z

Source-reported events for the cited work

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Observation 5d514e7e-7e90-4139-a01c-0fcc6303b071 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Explaining and Harnessing Adversarial Examples

Reference 11

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Observation ec9aaa32-a67f-4f3f-b6de-4bd8986fcc55 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 12

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Observation 00fcbbc0-7d2c-4e55-ba64-ca193a18f79e · outbound

This paper cites Simple Black-box Adversarial Attacks.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Simple Black-box Adversarial Attacks

Reference 13

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Observation fdf0e87b-2f61-4762-9e2f-66d3ca2402e0 · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Robust physical-world attacks on deep learning visual classification,

Reference 14

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Observation 8b86ea49-78c6-451f-a7a2-d4f35609b6e1 · outbound

This paper cites Runtime Stealthy Perception Attacks against DNN- Based Adaptive Cruise Control Systems,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Runtime Stealthy Perception Attacks against DNN- Based Adaptive Cruise Control Systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.690200Z

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Observation b55656f5-afeb-41f1-a694-72ad5c47ce73 · outbound

This paper cites Sensor and sensor fusion technology in autonomous vehicles: A review,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Sensor and sensor fusion technology in autonomous vehicles: A review,

Reference 16

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Observation 2b109e6d-1857-42e1-b4b2-daff35d8b49e · outbound

This paper cites Vehicle localization with low cost radar sensors,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Vehicle localization with low cost radar sensors,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.673364Z

Source-reported events for the cited work

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Observation 094422a4-ed5c-495a-87bb-3af06e245363 · outbound

This paper cites In-vehicle camera traffic sign detection and recognition,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems In-vehicle camera traffic sign detection and recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.662277Z

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Observation 7fffc0c0-3e72-46df-9bd1-3fbac64ce038 · outbound

This paper cites A deep learning approach to traffic lights: Detection, tracking, and classification,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems A deep learning approach to traffic lights: Detection, tracking, and classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.651145Z

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Observation e046caef-5a93-47fb-b691-e38e66e690e6 · outbound

This paper cites A real-time computer vision system for vehicle tracking and traffic surveillance,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems A real-time computer vision system for vehicle tracking and traffic surveillance,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.639101Z

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Observation 550fdc81-a4df-4b3d-a1ae-fde77d2ae9d0 · outbound

This paper cites A review of lidar sensor technologies for perception in automated driving,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems A review of lidar sensor technologies for perception in automated driving,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.626901Z

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Observation f22ba1b4-006d-443c-8e2d-ead9ed6dfa43 · outbound

This paper cites Adversarial Examples in Modern Machine Learning: A Review.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial Examples in Modern Machine Learning: A Review

Reference 22

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Observation 0f508d9c-dd30-4663-857b-14abcf75ea2e · outbound

This paper cites Adversarial attacks and coun- termeasures on image classification-based deep learning models in autonomous driving systems: A systematic review,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial attacks and coun- termeasures on image classification-based deep learning models in autonomous driving systems: A systematic review,

Reference 23

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Observation 618819aa-78a9-4515-bef2-475aaf96f540 · outbound

This paper cites Efficient defenses against adversarial attacks,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Efficient defenses against adversarial attacks,

Reference 24

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Observation 4833f3f3-8ca5-44eb-aec7-8aa4930b8b9a · outbound

This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Feature squeezing: Detecting adversarial examples in deep neural networks,

Reference 25

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Observation 7626ddde-8cdc-4233-bc21-d104a1a7a027 · outbound

This paper cites Mitigating Adversarial Effects Through Randomization.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Mitigating Adversarial Effects Through Randomization

Reference 26

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Observation 849d362b-3f5f-48ab-9cca-611925e5bfcc · outbound

This paper cites Adversarial Training: A Survey.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial Training: A Survey

Reference 27

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Observation fd452e14-ff8a-4ab8-bffe-ed21a0f55de8 · outbound

This paper cites Denoising diffusion models for plug-and-play image restoration,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Denoising diffusion models for plug-and-play image restoration,

Reference 28

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Observation 14f9874b-7ac2-4e31-9d3a-8c8a5fffdbec · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems A Simple Framework for Contrastive Learning of Visual Representations

Reference 29

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Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Car detection dataset,

Reference 30

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Observation 5d78c1b8-4d1c-4324-8d06-66fc77c3d291 · outbound

This paper cites Strategic resilience evaluation of neural networks within autonomous vehicle software,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Strategic resilience evaluation of neural networks within autonomous vehicle software,

Reference 31

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Observation 8987cb88-5fcd-478f-8ef7-5265202dd03c · outbound

This paper cites Strategic Safety-Critical Attacks against an Advanced Driver Assistance System,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Strategic Safety-Critical Attacks against an Advanced Driver Assistance System,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.557081Z

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Observation 44d64754-c76e-4aad-8ba2-181f94b6d45a · outbound

This paper cites A commute in data: The comma2k19 dataset,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems A commute in data: The comma2k19 dataset,

Reference 33

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This paper cites Improving Transformation Invariance in Contrastive Representation Learning.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Improving Transformation Invariance in Contrastive Representation Learning

Reference 34

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Observation df187cee-90d0-4c60-bc4b-a90c1d2319bb · outbound

This paper cites Poba-ga: Pertur- bation optimized black-box adversarial attacks via genetic algorithm,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Poba-ga: Pertur- bation optimized black-box adversarial attacks via genetic algorithm,

Reference 35

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Observation 9595a43f-90a3-4eaa-b56e-49b187f0faaf · outbound

This paper cites Robust roadside physical adversarial attack against deep learning in lidar perception modules,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Robust roadside physical adversarial attack against deep learning in lidar perception modules,

Reference 36

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Observation 6341b5b9-fb9f-45cd-9ec0-58e305e6bce5 · outbound

This paper cites Simultaneously optimizing perturbations and positions for black-box adversarial patch attacks,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Simultaneously optimizing perturbations and positions for black-box adversarial patch attacks,

Reference 37

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This paper cites Adversarial sticker: A stealthy attack method in the physical world,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial sticker: A stealthy attack method in the physical world,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.505457Z

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Observation 2ff9261f-7fcc-491b-b06f-b8e09a4f56e1 · outbound

This paper cites Adversarial Color Film: Effective Physical-World Attack to DNNs.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial Color Film: Effective Physical-World Attack to DNNs

Reference 39

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Observation 32b66c86-9b12-4966-a2c4-5ca7c587630e · outbound

This paper cites Targeted attention attack on deep learning models in road sign recognition,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Targeted attention attack on deep learning models in road sign recognition,

Reference 40

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This paper cites Too good to be safe: Tricking lane detection in autonomous driving with crafted perturbations,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Too good to be safe: Tricking lane detection in autonomous driving with crafted perturbations,

Reference 41

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This paper cites Attacking vision-based perception in end-to-end autonomous driving models,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Attacking vision-based perception in end-to-end autonomous driving models,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.479415Z

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Observation 137d3261-6ba8-4f44-bed5-0d43bce1e6a7 · outbound

This paper cites Physical hijacking attacks against object trackers,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Physical hijacking attacks against object trackers,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.467675Z

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Observation 2e5036bc-0d91-416e-b7b4-85934bb4ffab · outbound

This paper cites Advdo: Realistic adversarial attacks for trajectory prediction,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Advdo: Realistic adversarial attacks for trajectory prediction,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.456079Z

Source-reported events for the cited work

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Observation 9cb13ff6-d376-4902-8ba2-5c9a38983397 · outbound

This paper cites Physically realizable adversarial examples for lidar object detection,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Physically realizable adversarial examples for lidar object detection,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.445037Z

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Observation dc6537fa-3acb-4f72-a5a4-2c93ed44595d · outbound

This paper cites Beyond digital domain: Fooling deep learning based recognition system in physical world,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Beyond digital domain: Fooling deep learning based recognition system in physical world,

Reference 46

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Observation 7a5fd2be-8cd2-4c00-8c8d-61eb43189360 · outbound

This paper cites Dual attention suppression attack: Generate adversarial camouflage in physical world,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Dual attention suppression attack: Generate adversarial camouflage in physical world,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.426139Z

Source-reported events for the cited work

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Observation bd146f8b-13d3-41f7-9448-50ae59116293 · outbound

This paper cites Robustness testing of data and knowledge driven anomaly detection in cyber-physical systems,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Robustness testing of data and knowledge driven anomaly detection in cyber-physical systems,

Reference 48

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Source-reported events for the cited work

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This paper cites AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:45:13.143999Z

Source-reported events for the cited work

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Observation d4053acd-a361-4e52-a7e1-164712c38541 · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T21:45:13.079740Z

Source-reported events for the cited work

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Observation d4a88d96-970d-4373-8ba6-3c3f3c144e8b · outbound

This paper cites Defense against adversarial attacks using high-level representation guided denoiser,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Defense against adversarial attacks using high-level representation guided denoiser,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.399714Z

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Observation c75132c2-ac28-429f-a10e-9c066dfc422c · outbound

This paper cites Adversarial defense by stratified convolutional sparse coding,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial defense by stratified convolutional sparse coding,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.387365Z

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Observation 7bdd5fe8-dc00-47c6-93ac-7ce56d1da89b · outbound

This paper cites Defense against universal adversarial perturbations,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Defense against universal adversarial perturbations,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.375299Z

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Observation a647adce-e53b-4dbd-9b37-ad6d4d13976c · outbound

This paper cites Hybrid knowledge and data driven synthesis of runtime monitors for cyber- physical systems,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Hybrid knowledge and data driven synthesis of runtime monitors for cyber- physical systems,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.363547Z

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Observation 7d1649cb-04ad-4da6-a796-8fd156a63b2e · outbound

This paper cites Data-driven design of context-aware monitors for hazard pre- diction in artificial pancreas systems,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Data-driven design of context-aware monitors for hazard pre- diction in artificial pancreas systems,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.351826Z

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Observation 1f6d6be1-9123-4156-bb5f-2b1183858621 · outbound

This paper cites Safety interventions against adversarial patches in an open-source driver assistance system,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Safety interventions against adversarial patches in an open-source driver assistance system,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.340481Z

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Observation cf4c6b94-eaea-42f8-9e40-543df026db0b · outbound

This paper cites Adversarial attacks and defenses in deep learning for autonomous vehicles: A systematic review from a safety perspective,.

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems Adversarial attacks and defenses in deep learning for autonomous vehicles: A systematic review from a safety perspective,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:45:13.328440Z

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Pith citing papers

Observation cfe3fa2a-947d-4ceb-88da-a7bc22d5faf6 · inbound

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses cites this paper.

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems

Reference 55

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