Paper Citation Record · LEDGER

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection

As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.16358.

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

pith.paper-citation-record.v1
2412.16358 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:44:38.337600Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:44:38.208337Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T10:44:38.469763Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy20
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ad63552-b633-4c49-9f08-2a0c321bca56 · outbound

This paper cites State-of-the-art detec- tors, such as YOLO [1] and RetinaNet [2], which are based on deep neural networks (DNN), have become foundational in this domain.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection State-of-the-art detec- tors, such as YOLO [1] and RetinaNet [2], which are based on deep neural networks (DNN), have become foundational in this domain

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.813413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b4ddd7b-5565-46f2-a935-7e3609a1bf90 · outbound

This paper cites Szegedyet al.[3] introduced AAs to expose vulnerabilities in deep learning models.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Szegedyet al.[3] introduced AAs to expose vulnerabilities in deep learning models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.796860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ab1adaa-5cc2-4563-8add-02c2850e15a7 · outbound

This paper cites Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T10:44:38.475016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b02bf790-c846-4471-8ae4-8f814c3ff15a · outbound

This paper cites original.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection original

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.779661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ac5dd54-a1f5-4a63-a9ed-61ef787dde0e · outbound

This paper cites Given the high effec- tiveness of unconstrained AAs, there is limited room for im- provement.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Given the high effec- tiveness of unconstrained AAs, there is limited room for im- provement

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.762800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dfbcca90-ab52-4633-aac8-3865ddafafc5 · outbound

This paper cites We also study the performance-practicality trade-off when imple- menting adversarial camouflages.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection We also study the performance-practicality trade-off when imple- menting adversarial camouflages

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.714839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a67ab2e4-2138-4fcb-bb07-d592c46c4a5e · outbound

This paper cites Small Vehicles.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Small Vehicles

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.730347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 757294ad-a6e0-4b48-9dd0-5c80d0cd865c · outbound

This paper cites Adversarial Examples in the Physical World,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Adversarial Examples in the Physical World,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.606161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 802e9f58-0284-41e7-8409-5aad2a02a7b6 · outbound

This paper cites YOLOv5 by Ultralytics,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection YOLOv5 by Ultralytics,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.698989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e333635f-c426-4f99-8c4d-f0bfb140f954 · outbound

This paper cites Focal Loss for Dense Object Detection,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Focal Loss for Dense Object Detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.683711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 39756f85-9206-4fa9-a131-2d77c7a620cd · outbound

This paper cites More details on score as- signment are in Section S6 in the Supplementary Material.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection More details on score as- signment are in Section S6 in the Supplementary Material

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.746403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f22f981a-7483-4870-89a3-18a2781720ec · outbound

This paper cites Intriguing properties of neural networks.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Intriguing properties of neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T10:44:38.257170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b2d481-cbde-4894-b01d-a2114727c16a · outbound

This paper cites ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Uni- versal and Robust Vehicle Evasion,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Uni- versal and Robust Vehicle Evasion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.667786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 32542160-f027-4532-9a9d-e2002d2b9969 · outbound

This paper cites Ad- versarial Attacks on Aerial Imagery : The State-of-the- Art and Perspective,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Ad- versarial Attacks on Aerial Imagery : The State-of-the- Art and Perspective,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.652869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c0263b6-bea4-4e52-8d9b-97576945ed3d · outbound

This paper cites Ad- versarial Attacks against a Satellite-borne Multispectral Cloud Detector,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Ad- versarial Attacks against a Satellite-borne Multispectral Cloud Detector,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.637506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 25cb42a7-a15d-45f7-b554-26865370981b · outbound

This paper cites Robust Physical- World Attacks on Deep Learning Visual Classification,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Robust Physical- World Attacks on Deep Learning Visual Classification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.621953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 13173718-60f7-4df2-b36e-62387e2fe943 · outbound

This paper cites D3AdvM: A direct 3D adversarial sample attack inside mesh data,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection D3AdvM: A direct 3D adversarial sample attack inside mesh data,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.590940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ef1bf63a-7a3e-4e10-80b5-f315d14f10c0 · outbound

This paper cites ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T10:44:38.291672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab1a7b0d-66f5-4ef4-8c5e-9adf837d7a85 · outbound

This paper cites Physical Adversarial Attacks on an Aerial Imagery Object Detector,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Physical Adversarial Attacks on an Aerial Imagery Object Detector,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.574606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06a392bc-4ef1-41aa-92ed-b5521c5a7c3f · outbound

This paper cites TPH-YOLOv5: Improved YOLOv5 Based on Trans- former Prediction Head for Object Detection on Drone- captured Scenarios,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection TPH-YOLOv5: Improved YOLOv5 Based on Trans- former Prediction Head for Object Detection on Drone- captured Scenarios,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.558081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7a78fc8-ce59-4370-8832-317af5b527b5 · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Accelerating 3D Deep Learning with PyTorch3D

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T10:44:38.306149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbd9df42-1217-4613-99ca-8a0f18780785 · outbound

This paper cites Learning Generative Models of Textured 3D Meshes From Real-World Images,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Learning Generative Models of Textured 3D Meshes From Real-World Images,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.541844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1c9d710c-a711-4b0a-ad80-d529cc54822f · outbound

This paper cites DTA: Physical Camou- flage Attacks Using Differentiable Transformation Net- work,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection DTA: Physical Camou- flage Attacks Using Differentiable Transformation Net- work,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.524775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7739724-b80c-4383-a134-73e07d77ce3b · outbound

This paper cites EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:44:38.403977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c97a4285-e00a-4606-b795-6690cf6f7d4d · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Inpaint Anything: Segment Anything Meets Image Inpainting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T10:44:38.326346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 565066f7-18d7-4a41-929a-dc4d8603820b · outbound

This paper cites Blender Cycles,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Blender Cycles,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:44:38.507771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 805e7686-21f5-4073-a995-2ade23e29d5e · outbound

This paper cites Faster R-CNN: Towards Real-Time Object De- tection with Region Proposal Networks,.

Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection Faster R-CNN: Towards Real-Time Object De- tection with Region Proposal Networks,

Reference 27

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T10:44:38.491260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 3ab1adaa-5cc2-4563-8add-02c2850e15a7 · inbound