Paper Citation Record · LEDGER

Fooling a Real Car with Adversarial Traffic Signs

As of 14 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:1907.00374.

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

pith.paper-citation-record.v1
1907.00374 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T12:35:57.354399Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:18:04.056680Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T00:16:15.729268Z

Reference resolution

47 of 47 outbound references displayed

verified exact42
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b3b8f01e-feee-4a1d-a46b-314cd03c25c3 · outbound

This paper cites Lane detection and tracking using B -Snake.

Fooling a Real Car with Adversarial Traffic Signs Lane detection and tracking using B -Snake

Reference 1

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.627038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4fa04e2d-161c-4f40-9411-a8043624463e · outbound

This paper cites an unresolved cited work.

Fooling a Real Car with Adversarial Traffic Signs Unresolved cited work

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.618049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e23cb589-4981-49b5-83da-b46bb43ebbf2 · outbound

This paper cites Ultra -Low Complexity Block-Based Lane Detection and Departure Warning System.

Fooling a Real Car with Adversarial Traffic Signs Ultra -Low Complexity Block-Based Lane Detection and Departure Warning System

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.623472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 70ff1448-58f7-4f97-91e9-4d093355a01a · outbound

This paper cites Towards reliable traffic sign recognition.

Fooling a Real Car with Adversarial Traffic Signs Towards reliable traffic sign recognition

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.602636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3aa3a91-3fa9-4d54-a998-dc4a453232b0 · outbound

This paper cites Vision -based traffic sign detection and analysis for intelligent driver assistan ce systems: Perspectives and survey.

Fooling a Real Car with Adversarial Traffic Signs Vision -based traffic sign detection and analysis for intelligent driver assistan ce systems: Perspectives and survey

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.528758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c671a509-ff3f-4d44-a64c-4aa67a2e516c · outbound

This paper cites doi: 10.1016/j.neunet.2012.02.016.

Fooling a Real Car with Adversarial Traffic Signs doi: 10.1016/j.neunet.2012.02.016

Reference 6

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.539877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 143933ab-1907-48de-8a49-4f369a797e41 · outbound

This paper cites Traffic sign recognition with multi -scale convolutional networks.

Fooling a Real Car with Adversarial Traffic Signs Traffic sign recognition with multi -scale convolutional networks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.596919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 50c3ff1d-9e92-4c83-901b-fb1db7f44258 · outbound

This paper cites Obstacle detection for self -driving cars using only monocular cameras and wheel odometry.

Fooling a Real Car with Adversarial Traffic Signs Obstacle detection for self -driving cars using only monocular cameras and wheel odometry

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.572118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28f2362b-8fd8-454d-89a4-c1da67e28256 · outbound

This paper cites Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling.

Fooling a Real Car with Adversarial Traffic Signs Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.590037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 42bb1a09-6b68-44bb-88f7-1b935d78986a · outbound

This paper cites Enabling pedestrian safety using computer vision techniques: A case study of the 2018 uber inc.

Fooling a Real Car with Adversarial Traffic Signs Enabling pedestrian safety using computer vision techniques: A case study of the 2018 uber inc

Reference 10

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.544123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6d8693f-549c-4422-8a7f-d03d87be69c4 · outbound

This paper cites Energy -Efficient Resource Allocation for LTE -A Networks.

Fooling a Real Car with Adversarial Traffic Signs Energy -Efficient Resource Allocation for LTE -A Networks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.507673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10d3f3fb-8037-464d-93e4-6d0b69259d10 · outbound

This paper cites Looking at Humans in the Age of Self -Driving a nd Highly Automated Vehicles.

Fooling a Real Car with Adversarial Traffic Signs Looking at Humans in the Age of Self -Driving a nd Highly Automated Vehicles

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.550155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 933fb03b-d144-4485-9164-bd2d8bc62666 · outbound

This paper cites Towards fully autonomous driving: Systems and algorithms.

Fooling a Real Car with Adversarial Traffic Signs Towards fully autonomous driving: Systems and algorithms

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.608719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f5c1037a-385e-4249-89ba-d0b027de1dc6 · outbound

This paper cites The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.

Fooling a Real Car with Adversarial Traffic Signs The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.761073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf81adb6-facb-402a-9cc4-277404d531f6 · outbound

This paper cites Intriguing properties of neural networks.

Fooling a Real Car with Adversarial Traffic Signs Intriguing properties of neural networks

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.774984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7d3ea564-4d4f-4ecd-ba78-14408f302867 · outbound

This paper cites Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey.

Fooling a Real Car with Adversarial Traffic Signs Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.561452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d33b93a-c88a-4646-ba0e-4fc89fb8aa10 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

Fooling a Real Car with Adversarial Traffic Signs Towards Evaluating the Robustness of Neural Networks

Reference 17

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.516800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 26e871e8-388a-411b-a283-9b1f5651a71b · outbound

This paper cites Universal adversarial per turbations.

Fooling a Real Car with Adversarial Traffic Signs Universal adversarial per turbations

Reference 18

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.521251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b97006d-0d07-4877-a25e-ce332f4323a6 · outbound

This paper cites DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks.

Fooling a Real Car with Adversarial Traffic Signs DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

Reference 19

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.565588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb0e061b-f54e-4089-a7a3-85b810dd48dd · outbound

This paper cites Houdini: Fooling Deep Structured Prediction Models.

Fooling a Real Car with Adversarial Traffic Signs Houdini: Fooling Deep Structured Prediction Models

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.832340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7cec7157-d4f4-411e-b95c-54d742485c42 · outbound

This paper cites Exploring the Space of Black-box Attacks on Deep Neural Networks.

Fooling a Real Car with Adversarial Traffic Signs Exploring the Space of Black-box Attacks on Deep Neural Networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.251541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2cf7244-7174-4e5e-8f6f-7c3ae3403364 · outbound

This paper cites Exploring the Space of Black-box Attacks on Deep Neural Networks.

Fooling a Real Car with Adversarial Traffic Signs Exploring the Space of Black-box Attacks on Deep Neural Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.770648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 866217d9-9530-4d43-a82c-fb4625a60a21 · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Fooling a Real Car with Adversarial Traffic Signs Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.751335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e2e9f89d-bc27-46f8-85bb-33ef076c20ac · outbound

This paper cites The Space of Transferable Adversarial Examples.

Fooling a Real Car with Adversarial Traffic Signs The Space of Transferable Adversarial Examples

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.800146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d57d25ad-caf4-4b04-92b0-b3e128b86a8f · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

Fooling a Real Car with Adversarial Traffic Signs Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.247009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d5b3eb2f-5e18-46e0-b607-070746361e74 · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

Fooling a Real Car with Adversarial Traffic Signs Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.755749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e3917bd4-df42-4ab6-96a4-8d1892587cc6 · outbound

This paper cites On the Robustness of Semantic Segmentation Models to Adversarial Attacks.

Fooling a Real Car with Adversarial Traffic Signs On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.578550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8a01a8b5-46d2-4188-a857-dd0a6285f666 · outbound

This paper cites Physical Adversarial Examples for Object Detectors.

Fooling a Real Car with Adversarial Traffic Signs Physical Adversarial Examples for Object Detectors

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.828035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 61332dee-7415-473d-8e02-37e6a7d315a1 · outbound

This paper cites Synthesizing Robust Adversarial Examples.

Fooling a Real Car with Adversarial Traffic Signs Synthesizing Robust Adversarial Examples

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.793458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0e777d5b-530f-4726-8fee-e07be08a94fc · outbound

This paper cites DARTS: Deceiving Autonomous Cars with Toxic Signs.

Fooling a Real Car with Adversarial Traffic Signs DARTS: Deceiving Autonomous Cars with Toxic Signs

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.824018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ccca8cfe-d25c-4ed1-bba2-6a6f90ed3616 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Fooling a Real Car with Adversarial Traffic Signs Explaining and Harnessing Adversarial Examples

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.804361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd116884-3f75-4b9b-93cf-5ce3ebd484e7 · outbound

This paper cites Adversarial Machine Learning at Scale.

Fooling a Real Car with Adversarial Traffic Signs Adversarial Machine Learning at Scale

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.783519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5b6a5e4-d646-4568-88ed-465ae1fa464b · outbound

This paper cites Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning.

Fooling a Real Car with Adversarial Traffic Signs Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.815266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5606a7e5-80f7-4f8c-90b0-5e01ba77337e · outbound

This paper cites Learning with a Strong Adversary.

Fooling a Real Car with Adversarial Traffic Signs Learning with a Strong Adversary

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.764911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99f99213-cc03-48b9-811f-a3dc6298d3c3 · outbound

This paper cites Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization.

Fooling a Real Car with Adversarial Traffic Signs Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization

Reference 35

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.612330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4e23e29b-9774-4a26-b6b8-1feb8b8234c0 · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Fooling a Real Car with Adversarial Traffic Signs Ensemble Adversarial Training: Attacks and Defenses

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-25T12:36:57.742702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e58e4d20-ff31-4032-beef-13c143bc6ec0 · outbound

This paper cites Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.

Fooling a Real Car with Adversarial Traffic Signs Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Reference 37

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.582967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bb5d84bd-0c60-4059-85a1-bcc21f19f18e · outbound

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

Fooling a Real Car with Adversarial Traffic Signs Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.787582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5966a8d0-d674-4307-9dfd-1b4b4ba34534 · outbound

This paper cites Adversarial Examples Are Not Easily Detected.

Fooling a Real Car with Adversarial Traffic Signs Adversarial Examples Are Not Easily Detected

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T12:36:57.535597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 717fd24f-2b54-431c-87c3-9f1b9ceffeb2 · outbound

This paper cites Foveation-based Mechanisms Alleviate Adversarial Examples.

Fooling a Real Car with Adversarial Traffic Signs Foveation-based Mechanisms Alleviate Adversarial Examples

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.737908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a776c135-257b-4d12-b644-b944812a6c9a · outbound

This paper cites NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles.

Fooling a Real Car with Adversarial Traffic Signs NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.809973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a2674e2-0e06-42ab-9083-9a89905fa6ec · outbound

This paper cites Densely connected convolutional networks.

Fooling a Real Car with Adversarial Traffic Signs Densely connected convolutional networks

Reference 42

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.513029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 16b85ac4-8464-4b52-ad28-7f7476c71d77 · outbound

This paper cites Robust Physical-World Attacks on Deep Learning Models.

Fooling a Real Car with Adversarial Traffic Signs Robust Physical-World Attacks on Deep Learning Models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.242720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 728c894e-ec04-42c0-b4ee-a4f4301fca3c · outbound

This paper cites Robust Physical-World Attacks on Deep Learning Models.

Fooling a Real Car with Adversarial Traffic Signs Robust Physical-World Attacks on Deep Learning Models

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.746855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af10a902-9f76-4c07-ad89-e507dfd31fbf · outbound

This paper cites ShapeShifter: Robust physical adversarial attack on faster R-CNN object detector.

Fooling a Real Car with Adversarial Traffic Signs ShapeShifter: Robust physical adversarial attack on faster R-CNN object detector

Reference 45

Resolution: verified exact; doi, observed 2026-05-25T12:36:57.555263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3735ea3-3c84-4beb-a682-09881cb28023 · outbound

This paper cites Investigating Human Priors for Playing Video Games.

Fooling a Real Car with Adversarial Traffic Signs Investigating Human Priors for Playing Video Games

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-05-25T12:36:57.820013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b8aad64-a182-422f-adff-657353350e6d · outbound

This paper cites Experimental Security Research of Tesla Autopilot .; 2019.

Fooling a Real Car with Adversarial Traffic Signs Experimental Security Research of Tesla Autopilot .; 2019

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T12:36:58.238772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation ab8633e3-a99b-4a04-ba70-9d3bd4458943 · inbound

Learning Fair Robustness via Domain Mixup cites this paper.

Learning Fair Robustness via Domain Mixup Fooling a Real Car with Adversarial Traffic Signs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.056680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edd5f0df-0016-4ca2-bb56-5b3efeea8025 · inbound

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training cites this paper.

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training Fooling a Real Car with Adversarial Traffic Signs

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T21:36:58.238971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.