Paper Citation Record · LEDGER

Adversarial Attention Perturbations for Large Object Detection Transformers

As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2508.02987.

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

pith.paper-citation-record.v1
2508.02987 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:50:41.467985Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-17T02:36:16.437850Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T02:38:53.929427Z

Reference resolution

44 of 44 outbound references displayed

verified exact1
verified fuzzy35
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b512809-7824-4cba-8227-2d70708eac34 · outbound

This paper cites an unresolved cited work.

Adversarial Attention Perturbations for Large Object Detection Transformers Unresolved cited work

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 511110d8-fc30-40a9-99b6-10e507bc1d3b · outbound

This paper cites Align-detr: Improving detr with sim- ple iou-aware bce loss, 2023.

Adversarial Attention Perturbations for Large Object Detection Transformers Align-detr: Improving detr with sim- ple iou-aware bce loss, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.900325Z

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Observation 706a60d7-a672-4a27-b86b-914200431043 · outbound

This paper cites Salman Asif.

Adversarial Attention Perturbations for Large Object Detection Transformers Salman Asif

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.891174Z

Source-reported events for the cited work

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Observation c64cb791-32d1-445d-ad95-55c04478c62a · outbound

This paper cites End-to-end object detection with transformers.

Adversarial Attention Perturbations for Large Object Detection Transformers End-to-end object detection with transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.881917Z

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Observation b4d9f301-eda8-4957-a773-f9ace99a2477 · outbound

This paper cites Rele- vance attack on detectors.

Adversarial Attention Perturbations for Large Object Detection Transformers Rele- vance attack on detectors

Reference 6

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Source-reported events for the cited work

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Observation e6b48724-7185-45b3-a9d3-6c06b7aa6db4 · outbound

This paper cites Understanding ob- ject detection through an adversarial lens.

Adversarial Attention Perturbations for Large Object Detection Transformers Understanding ob- ject detection through an adversarial lens

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.862632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b28ffc3a-282b-4ddc-952e-d2625587bdee · outbound

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

Adversarial Attention Perturbations for Large Object Detection Transformers Adversarial objectness gradient attacks in real- time object detection systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.852845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3257702-bb6d-4f78-81de-80c38bc5b48f · outbound

This paper cites Object detection on coco test- dev.

Adversarial Attention Perturbations for Large Object Detection Transformers Object detection on coco test- dev

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.843837Z

Source-reported events for the cited work

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Observation a148af88-ac3d-4b12-b2ff-a6725219fe15 · outbound

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Adversarial Attention Perturbations for Large Object Detection Transformers Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation 3ba9e894-b14d-4fae-87cd-88aa7c59178f · outbound

This paper cites EVA: Exploring the Limits of Masked Visual Representation Learning at Scale.

Adversarial Attention Perturbations for Large Object Detection Transformers EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T04:50:41.326740Z

Source-reported events for the cited work

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Observation 96e0d3b4-bb09-4216-bbb9-f22931c60d60 · outbound

This paper cites Deep residual learning for image recognition.

Adversarial Attention Perturbations for Large Object Detection Transformers Deep residual learning for image recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.825190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f0568a9a-0176-40c8-9cbc-8fec4b0bacf3 · outbound

This paper cites Object-aware transfer-based black-box adversarial attack on object detector.

Adversarial Attention Perturbations for Large Object Detection Transformers Object-aware transfer-based black-box adversarial attack on object detector

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.816047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ecc5d21c-107e-4d4e-8962-b091c120cc69 · outbound

This paper cites Improving transferable adversarial attack for vision transformers via global attention and local drop.

Adversarial Attention Perturbations for Large Object Detection Transformers Improving transferable adversarial attack for vision transformers via global attention and local drop

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.806679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f44b9244-c065-4ccc-ac3b-76c79219b6a0 · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection, 2022.

Adversarial Attention Perturbations for Large Object Detection Transformers Exploring plain vision transformer backbones for object de- tection, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.797511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 78621324-8e7c-46fd-807d-59a135413a55 · outbound

This paper cites Robust adversarial perturbation on deep proposal-based models.

Adversarial Attention Perturbations for Large Object Detection Transformers Robust adversarial perturbation on deep proposal-based models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.788252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 74b0d3fd-47aa-42f0-ae68-8e0f614fbf48 · outbound

This paper cites Attack anything: Blind dnns via universal background adversarial attack, 2024.

Adversarial Attention Perturbations for Large Object Detection Transformers Attack anything: Blind dnns via universal background adversarial attack, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.779417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 06a2774f-b34e-4a03-8253-9245e9695ee9 · outbound

This paper cites A large-scale multiple-objective method for black-box attack against object detection.

Adversarial Attention Perturbations for Large Object Detection Transformers A large-scale multiple-objective method for black-box attack against object detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.769553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 02102c18-50bf-4723-8a86-e13fe6b184df · outbound

This paper cites Lawrence Zitnick.

Adversarial Attention Perturbations for Large Object Detection Transformers Lawrence Zitnick

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.760590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c5873ae-89b4-49eb-94cb-87b3858abf8f · outbound

This paper cites an unresolved cited work.

Adversarial Attention Perturbations for Large Object Detection Transformers Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 1f029a17-f496-4938-8260-194c8604a055 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Adversarial Attention Perturbations for Large Object Detection Transformers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.742360Z

Source-reported events for the cited work

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Observation d3d4d179-9997-4a0a-85e7-350e0045cdfc · outbound

This paper cites A convnet for the 2020s, 2022.

Adversarial Attention Perturbations for Large Object Detection Transformers A convnet for the 2020s, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.733568Z

Source-reported events for the cited work

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Observation da185219-98da-48b5-844d-4564a3f7fcbe · outbound

This paper cites Give me your attention: Dot-product attention considered harmful for adversarial patch robustness.

Adversarial Attention Perturbations for Large Object Detection Transformers Give me your attention: Dot-product attention considered harmful for adversarial patch robustness

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.724542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fca71beb-a15c-4fc4-b21a-6c95d6d74ca3 · outbound

This paper cites Towards deep learn- ing models resistant to adversarial attacks.

Adversarial Attention Perturbations for Large Object Detection Transformers Towards deep learn- ing models resistant to adversarial attacks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.714832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 863f7401-c90c-4160-aeb3-702d70b797f1 · outbound

This paper cites A Survey and Evaluation of Adversarial Attacks for Object Detection.

Adversarial Attention Perturbations for Large Object Detection Transformers A Survey and Evaluation of Adversarial Attacks for Object Detection

Reference 25

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Source-reported events for the cited work

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Observation 5eec3291-ca72-402c-ab16-2de04b35f4e0 · outbound

This paper cites NMS Strikes Back.

Adversarial Attention Perturbations for Large Object Detection Transformers NMS Strikes Back

Reference 26

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Source-reported events for the cited work

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Observation 7a760d4d-fbd0-4f04-ae50-cc199cbb9047 · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.

Adversarial Attention Perturbations for Large Object Detection Transformers Pytorch: An im- perative style, high-performance deep learning library

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.705848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4aa7f4a7-fa79-4fdd-b17f-99693e9f37cf · outbound

This paper cites Yolov3: An incremental improvement, 2018.

Adversarial Attention Perturbations for Large Object Detection Transformers Yolov3: An incremental improvement, 2018

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.696460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b2d35e48-6ecf-4007-8715-d6c5220f46e5 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks, 2016.

Adversarial Attention Perturbations for Large Object Detection Transformers Faster r-cnn: Towards real-time object detection with region proposal networks, 2016

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.687885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a18c4129-8942-4c19-97be-2eba6877ef32 · outbound

This paper cites detrex: Benchmarking de- tection transformers, 2023.

Adversarial Attention Perturbations for Large Object Detection Transformers detrex: Benchmarking de- tection transformers, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.677820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1ef4f24-3daa-4326-aa26-737a68f2e8bb · outbound

This paper cites Attention is all you need.

Adversarial Attention Perturbations for Large Object Detection Transformers Attention is all you need

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.668752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 55c4d454-7b08-46f4-8b37-487d1a93f090 · outbound

This paper cites InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions.

Adversarial Attention Perturbations for Large Object Detection Transformers InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions

Reference 32

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Source-reported events for the cited work

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Observation 750ef739-9c5f-46c9-9105-9b367dc56f11 · outbound

This paper cites Gradient-guided hierarchical feature attack for object detector.

Adversarial Attention Perturbations for Large Object Detection Transformers Gradient-guided hierarchical feature attack for object detector

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.659224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d605d2f-4e8f-43b9-8c2e-53a23f58b558 · outbound

This paper cites Transferable adversarial attacks for image and video object detection.

Adversarial Attention Perturbations for Large Object Detection Transformers Transferable adversarial attacks for image and video object detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.649795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d91d781d-e4d0-4732-8f1a-3763f85c5879 · outbound

This paper cites Detectron2.

Adversarial Attention Perturbations for Large Object Detection Transformers Detectron2

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.640209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4b530210-21f0-451a-a029-520e843de572 · outbound

This paper cites Adversarial examples for se- mantic segmentation and object detection.

Adversarial Attention Perturbations for Large Object Detection Transformers Adversarial examples for se- mantic segmentation and object detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.630701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5bba6505-4b89-47b4-b060-f2ad1e239b4f · outbound

This paper cites Focal modulation networks, 2022.

Adversarial Attention Perturbations for Large Object Detection Transformers Focal modulation networks, 2022

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.621481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 15a99c6d-f561-467b-918a-dcbd65956df3 · outbound

This paper cites Ni, and Heung-Yeung Shum.

Adversarial Attention Perturbations for Large Object Detection Transformers Ni, and Heung-Yeung Shum

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.611776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 42d23715-e3a9-46c3-a683-585d2972483a · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Adversarial Attention Perturbations for Large Object Detection Transformers Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T04:50:41.449475Z

Source-reported events for the cited work

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Observation d06082ba-0864-4956-a4ac-7e4795c9f281 · outbound

This paper cites • For AFOG’s implementation a link to an anonymous downloadable source repository is included in our ab- stract.

Adversarial Attention Perturbations for Large Object Detection Transformers • For AFOG’s implementation a link to an anonymous downloadable source repository is included in our ab- stract

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.602073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 83526d91-e760-438e-93b6-0a5e6ef5cbe0 · outbound

This paper cites an unresolved cited work.

Adversarial Attention Perturbations for Large Object Detection Transformers Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T04:50:41.592101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 313b3f1b-e7a1-4bcf-b8ec-a315d8f5c391 · outbound

This paper cites A larger version of InternImage is also cur- rently one of the strongest models on the COCO ob- ject detection leaderboard [9].

Adversarial Attention Perturbations for Large Object Detection Transformers A larger version of InternImage is also cur- rently one of the strongest models on the COCO ob- ject detection leaderboard [9]

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.572754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0a2a757e-dc4f-4e43-856a-2c76c73275c1 · outbound

This paper cites The key difference between AFOG and AFOG-V is the replacement of Ox with a set of zero predictions ∅ instead of forward propagating image Ox ← fD(x; ϑ).

Adversarial Attention Perturbations for Large Object Detection Transformers The key difference between AFOG and AFOG-V is the replacement of Ox with a set of zero predictions ∅ instead of forward propagating image Ox ← fD(x; ϑ)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.563389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 734b680c-9d11-448f-bf75-dadb7965eb06 · outbound

This paper cites Couch” prediction in both models by disrupting both class and bounding box losses. Similarly, AFOG induces several small “Cat.

Adversarial Attention Perturbations for Large Object Detection Transformers Couch” prediction in both models by disrupting both class and bounding box losses. Similarly, AFOG induces several small “Cat

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.552576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3e2dfd36-a394-4637-9854-ebdc06f28cc6 · outbound

This paper cites We choose ViTDet for our experiments to investigate AFOG’s potential applicability to all ViT-based models.

Adversarial Attention Perturbations for Large Object Detection Transformers We choose ViTDet for our experiments to investigate AFOG’s potential applicability to all ViT-based models

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:50:41.582447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 54a19a12-eb9e-4dcd-92a7-88ce5ffa8497 · inbound

Out-of-the-box: Black-box Causal Attacks on Object Detectors cites this paper.

Out-of-the-box: Black-box Causal Attacks on Object Detectors Adversarial Attention Perturbations for Large Object Detection Transformers

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:38:53.931560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.