Paper Citation Record · LEDGER

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks

As of 11 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2502.02537.

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

pith.paper-citation-record.v1
2502.02537 v1

Coverage vector

measured 48 of 48 reference resolution

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T04:59:24.293335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T04:59:36.234700Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation e4b98466-ab09-4b26-8447-40abcb2d46a3 · outbound

This paper cites Lidar spoofing attack detection in autonomous vehicles.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Lidar spoofing attack detection in autonomous vehicles

Reference 1

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Observation 9199221f-283b-4130-b537-ea164d161c53 · outbound

This paper cites Adversarial objectness gradient at- tacks in real-time object detection systems.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adversarial objectness gradient at- tacks in real-time object detection systems

Reference 4

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Observation fcf788b9-6b4c-4a93-90b9-89c9cca9ff1c · outbound

This paper cites Detecting Adversarial Samples from Artifacts.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Detecting Adversarial Samples from Artifacts

Reference 7

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Observation 0d9e6552-2af2-451e-965e-683b15d654cc · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Explaining and Harnessing Adversarial Examples

Reference 9

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Observation 15d54871-1cc5-4338-b3cb-00877a7e48b6 · outbound

This paper cites Bounding box re- gression with uncertainty for accurate object detection.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Bounding box re- gression with uncertainty for accurate object detection

Reference 11

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Observation 405e3c3a-a442-4541-b931-7ade4b911178 · outbound

This paper cites Ad- versarial attack and defense of yolo detectors in au- tonomous driving scenarios.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Ad- versarial attack and defense of yolo detectors in au- tonomous driving scenarios

Reference 13

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Observation 7f522be4-fefa-4717-a9b1-83c34f5af398 · outbound

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Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Unresolved cited work

Reference 14

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Observation 4dc1c03c-6b04-4fa3-81e8-1d000f9606cc · outbound

This paper cites Dropout infer- ence in bayesian neural networks with alpha-divergences.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Dropout infer- ence in bayesian neural networks with alpha-divergences

Reference 15

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This paper cites Connecting the dots: Detecting adversarial perturbations using context inconsis- tency.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Connecting the dots: Detecting adversarial perturbations using context inconsis- tency

Reference 16

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Observation cdd053f7-9b50-41b2-8e2e-9db8eb7cd78d · outbound

This paper cites Learn- ing distilled collaboration graph for multi-agent percep- tion.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Learn- ing distilled collaboration graph for multi-agent percep- tion

Reference 17

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Observation 366394e8-0906-4afd-a5aa-99be895273d4 · outbound

This paper cites V2x-sim: Multi-agent collaborative perception dataset and bench- mark for autonomous driving.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks V2x-sim: Multi-agent collaborative perception dataset and bench- mark for autonomous driving

Reference 18

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Observation 56c6f97d-7254-48bd-9908-aef446078764 · outbound

This paper cites Among us: Adversarially robust collaborative perception by consensus.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Among us: Adversarially robust collaborative perception by consensus

Reference 19

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Observation 52a10b22-b1ca-4a78-9963-d8d8cb375ca7 · outbound

This paper cites CoMamba: Real-time Cooperative Perception Unlocked with State Space Models.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks CoMamba: Real-time Cooperative Perception Unlocked with State Space Models

Reference 20

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Observation b80b50ba-e50a-43fb-abda-c5c863f710ee · outbound

This paper cites Adversarial Examples that Fool Detectors.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adversarial Examples that Fool Detectors

Reference 22

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Observation 1d58798f-0847-4848-bf22-eb7630c1d72b · outbound

This paper cites Probabilistic object detection via deep ensembles.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Probabilistic object detection via deep ensembles

Reference 23

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Observation c91b14af-dd25-48af-b292-657f083f4c5f · outbound

This paper cites Detecting adversarial attacks on audiovisual speech recognition.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Detecting adversarial attacks on audiovisual speech recognition

Reference 24

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Observation f2223990-9c86-4229-994e-135421b13c21 · outbound

This paper cites Uncertainty-based detection of adversarial attacks in se- mantic segmentation.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Uncertainty-based detection of adversarial attacks in se- mantic segmentation

Reference 25

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Observation 2c714967-a727-40b0-af88-323ebdf67e0a · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 26

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This paper cites Learning an uncertainty-aware object de- tector for autonomous driving.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Learning an uncertainty-aware object de- tector for autonomous driving

Reference 27

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Observation d70b20bd-50d3-4cd9-b25c-f27ecdbe49e5 · outbound

This paper cites Dropout sampling for ro- bust object detection in open-set conditions.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Dropout sampling for ro- bust object detection in open-set conditions

Reference 28

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Observation 4d79c3d4-3cf7-4815-a909-9ab7cdf37a54 · outbound

This paper cites Adversarial Phenomenon in the Eyes of Bayesian Deep Learning.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adversarial Phenomenon in the Eyes of Bayesian Deep Learning

Reference 30

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This paper cites 3d semantic scene completion: A survey.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks 3d semantic scene completion: A survey

Reference 31

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Observation 651bce6e-70e8-4b8b-a244-5e41418da207 · outbound

This paper cites Using uncertainty as a defense against adversarial attacks for tabular datasets.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Using uncertainty as a defense against adversarial attacks for tabular datasets

Reference 32

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Observation 08a2fc7d-1dbe-499b-855e-b98c9efc5533 · outbound

This paper cites A tutorial on conformal prediction.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks A tutorial on conformal prediction

Reference 33

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This paper cites Uncertainty quantification of collaborative detection for self-driving.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Uncertainty quantification of collaborative detection for self-driving

Reference 35

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Observation fa54b79d-8be6-4fb5-8853-01cafc541c4a · outbound

This paper cites Collab- orative multi-object tracking with conformal uncertainty propagation.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Collab- orative multi-object tracking with conformal uncertainty propagation

Reference 36

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This paper cites Towards robust {LiDAR-based} per- ception in autonomous driving: General black-box adversar- ial sensor attack and countermeasures.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Towards robust {LiDAR-based} per- ception in autonomous driving: General black-box adversar- ial sensor attack and countermeasures

Reference 37

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Observation a70e6a8f-b1fa-4a47-a6f2-c69ef7b85d05 · outbound

This paper cites Adversarial attacks on multi-agent communication.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adversarial attacks on multi-agent communication

Reference 38

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Observation acf786ab-5214-4bd1-8e83-fd17b6a74185 · outbound

This paper cites V2vnet: Vehicle-to-vehicle communi- cation for joint perception and prediction.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks V2vnet: Vehicle-to-vehicle communi- cation for joint perception and prediction

Reference 39

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This paper cites Characterizing adver- sarial examples based on spatial consistency information for semantic segmentation.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Characterizing adver- sarial examples based on spatial consistency information for semantic segmentation

Reference 40

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This paper cites Advit: Adversarial frames identifier based on temporal consistency in videos.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Advit: Adversarial frames identifier based on temporal consistency in videos

Reference 41

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Observation a87733d3-c089-47b0-8f3d-91738ade1583 · outbound

This paper cites Adversarial examples for semantic segmentation and object detection.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adversarial examples for semantic segmentation and object detection

Reference 42

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Observation 640bdb9e-3ddb-4625-8c5f-dd0efe7d3dbb · outbound

This paper cites Bridging the Domain Gap for Multi-Agent Perception.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Bridging the Domain Gap for Multi-Agent Perception

Reference 43

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This paper cites V2x-vit: Vehicle-to-everything cooperative perception with vision transformer.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks V2x-vit: Vehicle-to-everything cooperative perception with vision transformer

Reference 44

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This paper cites Uncertainty-aware sar atr: Defending against adversarial attacks via bayesian neural networks.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Uncertainty-aware sar atr: Defending against adversarial attacks via bayesian neural networks

Reference 45

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Observation 6a6f41ca-88a6-4e5c-bdac-98fa4b4504e8 · outbound

This paper cites Adc: Adversarial attacks against object de- tection that evade context consistency checks.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Adc: Adversarial attacks against object de- tection that evade context consistency checks

Reference 46

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Observation 5ea62881-d93e-4d30-a893-1f25f39a9978 · outbound

This paper cites Towards adversarially robust object detection.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Towards adversarially robust object detection

Reference 47

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This paper cites Improving generalization of adversarial training via robust critical fine-tuning.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Improving generalization of adversarial training via robust critical fine-tuning

Reference 48

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Observation 44711a9a-99f5-4b2b-ae11-477d0a8a4912 · outbound

This paper cites Understanding Measures of Uncertainty for Adversarial Example Detection.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Understanding Measures of Uncertainty for Adversarial Example Detection

Reference 2008

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This paper cites Shadow-catcher: Looking into shadows to detect ghost objects in au- tonomous vehicle 3d sensing.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Shadow-catcher: Looking into shadows to detect ghost objects in au- tonomous vehicle 3d sensing

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:55:41.424944Z

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This paper cites A review and comparative study on probabilistic object detection in autonomous driving.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks A review and comparative study on probabilistic object detection in autonomous driving

Reference 2017

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Observation 8a4f98fe-bd6d-473c-89be-eb3ae3e148ef · outbound

This paper cites Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift

Reference 2018

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Observation 9ed41101-d2b0-40ab-bf84-0bf1f601b527 · outbound

This paper cites Robust multi- agent reinforcement learning with state uncertainty.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Robust multi- agent reinforcement learning with state uncertainty

Reference 2019

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Observation 8a178b14-84f8-4260-b073-d6c73f8b19e8 · outbound

This paper cites Guaranteeing safety of learned perception modules via measurement-robust con- trol barrier functions.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Guaranteeing safety of learned perception modules via measurement-robust con- trol barrier functions

Reference 2020

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Observation cfd9173e-f180-4289-a938-ba1e0b5ca945 · outbound

This paper cites Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training

Reference 2021

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Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Conformal PID Control for Time Series Prediction

Reference 2022

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This paper cites Analyzing infrastructure lidar placement with realistic lidar simulation library.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks Analyzing infrastructure lidar placement with realistic lidar simulation library

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:55:41.464935Z

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Observation 2dc1904f-e36c-4a41-a13b-5138343f1f9a · outbound

This paper cites When2com: Multi-agent percep- tion via communication graph grouping.

Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks When2com: Multi-agent percep- tion via communication graph grouping

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:55:41.324336Z

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Pith citing papers

Observation 24d73ba0-108c-437b-8068-2e5eb723a319 · inbound

Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation cites this paper.

Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks

Reference 20

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