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Paper Citation Record · LEDGER

Evaluating the Adversarial Robustness of Detection Transformers

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2412.18718.

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

pith.paper-citation-record.v1
2412.18718 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:36:16.819416Z

measured 30 of 30 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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  • verified fuzzy9
  • unresolved20
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation fe6a3936-355a-4c15-804a-4f83592d92bc · outbound

This paper cites Object Detection in Autonomous Vehicles: Status and Open Challenges.

Evaluating the Adversarial Robustness of Detection Transformers Object Detection in Autonomous Vehicles: Status and Open Challenges

Reference 1

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Observation b2a87a32-af3c-42c4-bc90-4fe9b48ae0c3 · outbound

This paper cites Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming.

Evaluating the Adversarial Robustness of Detection Transformers Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Reference 2

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Observation e4dc20bb-0114-4f7f-a2bd-b15a7d935aa4 · outbound

This paper cites Attention is all you need,.

Evaluating the Adversarial Robustness of Detection Transformers Attention is all you need,

Reference 3

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Observation a1ec70c7-315b-44fe-b5f0-967ec0f40cee · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Evaluating the Adversarial Robustness of Detection Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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Observation 19df2f67-b808-431a-ad2f-36012a947219 · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers End-to-end object detection with transformers,

Reference 5

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source=pdf_text observed=2026-08-11T04:36:16.701649Z digest=sha256:22166a9f7f1540b06adb32061b4318fb29427b41e3269da85ef6730ce48cc729

Observation 1868163f-df0d-45bd-b01b-bce933dd1a85 · outbound

This paper cites Intriguing properties of neural networks.

Evaluating the Adversarial Robustness of Detection Transformers Intriguing properties of neural networks

Reference 6

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Observation ff7cc611-eee1-42ef-a70c-12ebc6e92e6b · outbound

This paper cites Learning ordered top-k adversarial attacks via adversarial distillation,.

Evaluating the Adversarial Robustness of Detection Transformers Learning ordered top-k adversarial attacks via adversarial distillation,

Reference 7

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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.

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Observation 1f8d8731-94dd-499a-988c-8dea7c795290 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Evaluating the Adversarial Robustness of Detection Transformers Explaining and Harnessing Adversarial Examples

Reference 8

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source=pdf_text observed=2026-08-11T04:36:16.716834Z digest=sha256:9dd6429870460d4f63393e2edec14a276d3e5f5d6fcf02674050c9b0ae19118b

Observation b0fde5fc-b64e-432b-b33e-99e3edff39f3 · outbound

This paper cites Towards the Science of Security and Privacy in Machine Learning.

Evaluating the Adversarial Robustness of Detection Transformers Towards the Science of Security and Privacy in Machine Learning

Reference 9

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Observation e2920700-db99-4695-95dc-1d56142b3924 · outbound

This paper cites On the robustness of vision transformers to adversarial examples,.

Evaluating the Adversarial Robustness of Detection Transformers On the robustness of vision transformers to adversarial examples,

Reference 10

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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.

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Observation a18ef748-11ff-4951-af76-e444dda4cd58 · outbound

This paper cites On the Adversarial Robustness of Vision Transformers.

Evaluating the Adversarial Robustness of Detection Transformers On the Adversarial Robustness of Vision Transformers

Reference 11

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Observation fde17cf2-bde2-4adc-8b87-cb349fdfdc6d · outbound

This paper cites Detects ec: Evaluating the robustness of object detection models to adversarial attacks,.

Evaluating the Adversarial Robustness of Detection Transformers Detects ec: Evaluating the robustness of object detection models to adversarial attacks,

Reference 12

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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.

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Observation d9e7dec0-4b8b-4105-a84f-08ce971d2901 · outbound

This paper cites Adversarial attacks on faster r-cnn object detector,.

Evaluating the Adversarial Robustness of Detection Transformers Adversarial attacks on faster r-cnn object detector,

Reference 13

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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.

source=pdf_text observed=2026-08-11T04:36:16.740866Z digest=sha256:ecd83119002110c8641efa94aba73e825fe25dad3513db0be6bbcdb566ca47c5

Observation d0d32951-dad4-4b1b-bc1d-4333ee2439dc · outbound

This paper cites Anchor detr: Query design for transformer-based detector,.

Evaluating the Adversarial Robustness of Detection Transformers Anchor detr: Query design for transformer-based detector,

Reference 14

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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.

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Observation 20d21516-0a08-4632-bf41-272d9ef6becd · outbound

This paper cites Efficient DETR: Improving End-to-End Object Detector with Dense Prior.

Evaluating the Adversarial Robustness of Detection Transformers Efficient DETR: Improving End-to-End Object Detector with Dense Prior

Reference 15

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source=pdf_text observed=2026-08-11T04:36:16.750140Z digest=sha256:7fa91c8a02b5968fd73e40b21495a7adce9a14fba4bbae064a1294a7425b7171

Observation 06c1f0d3-208f-4408-9d3b-52ee58ccb97c · outbound

This paper cites Transferable adversarial attacks on vision transformers with token gradient regularization,.

Evaluating the Adversarial Robustness of Detection Transformers Transferable adversarial attacks on vision transformers with token gradient regularization,

Reference 16

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source=pdf_text observed=2026-08-11T04:36:16.754940Z digest=sha256:28591ac9aa1973b5362722911e2cc7967dd2793ed6080a8bdcdf7e27c915ffc2

Observation 7b63b369-8b68-41b7-a10e-27d3169e8f48 · outbound

This paper cites Towards transferable adversarial attacks on vision transformers,.

Evaluating the Adversarial Robustness of Detection Transformers Towards transferable adversarial attacks on vision transformers,

Reference 17

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raw_fallback, observed 2026-08-11T04:36:17.153416Z

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.

source=pdf_text observed=2026-08-11T04:36:16.759327Z digest=sha256:b89bf560b9ed7795450305a36fdf032d3c9549dce02ef4ba0a2a3029f3fecb57

Observation eac37712-702b-42a7-94fe-149b860f6cb9 · outbound

This paper cites Generating transferable adversarial examples against vision trans- formers,.

Evaluating the Adversarial Robustness of Detection Transformers Generating transferable adversarial examples against vision trans- formers,

Reference 18

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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.

source=pdf_text observed=2026-08-11T04:36:16.763796Z digest=sha256:96f4edc4e7f5e2e2e55e2e32d3cdf130e7201f40cb1fafb0c1455d1449c049a9

Observation 1bcc6b94-ac63-4b7b-b41b-abe8c16c0799 · outbound

This paper cites Big transfer (bit): General visual representation learn- ing,.

Evaluating the Adversarial Robustness of Detection Transformers Big transfer (bit): General visual representation learn- ing,

Reference 19

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

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Observation 1fdb3f7a-1ab3-4d12-ace9-525a0f750922 · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 20

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Observation fa5ff650-e556-4cb9-acdf-75befa86a58d · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

Evaluating the Adversarial Robustness of Detection Transformers Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 21

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Observation dd7789a3-1e0a-4dde-9d4c-c0c17d00e112 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Evaluating the Adversarial Robustness of Detection Transformers Towards evaluating the robustness of neural networks,

Reference 22

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Observation 02c06078-2bb9-48fd-91c6-08ea7226611a · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers Give me your attention: Dot-product attention considered harmful for adversarial patch robustness,

Reference 23

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

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Observation 1a9bc561-287e-47c9-8e40-ef57705642c5 · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers Object-aware transfer-based black-box adversarial attack on object detector,

Reference 24

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Observation b4857418-0e6d-4452-9410-c12114f334a4 · outbound

This paper cites Transferable Physical Attack against Object Detection with Separable Attention.

Evaluating the Adversarial Robustness of Detection Transformers Transferable Physical Attack against Object Detection with Separable Attention

Reference 25

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local_arxiv, observed 2026-08-11T04:36:16.879219Z

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

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Observation 35aec70c-2213-4ebc-992f-2e6e6590091b · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 26

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Observation 543fe5fb-3af7-4ec9-a78a-4fbf20d4e254 · outbound

This paper cites Ssd: Single shot multibox detector,.

Evaluating the Adversarial Robustness of Detection Transformers Ssd: Single shot multibox detector,

Reference 27

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Observation 6418cc7c-c2fa-4257-b6bb-ddd37eea057a · outbound

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

Evaluating the Adversarial Robustness of Detection Transformers Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 28

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Observation fcd99b3d-6a17-42a8-a4de-a0c09c04b226 · outbound

This paper cites Microsoft coco: Common objects in context,.

Evaluating the Adversarial Robustness of Detection Transformers Microsoft coco: Common objects in context,

Reference 29

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Observation 59527074-c398-495a-af58-7543a1143a60 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Evaluating the Adversarial Robustness of Detection Transformers Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 30

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Pith citing papers

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