Paper Citation Record · LEDGER

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2608.06674.

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

pith.paper-citation-record.v1
2608.06674 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:46:06.914961Z

measured 37 of 37 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 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

37 of 37 outbound references displayed

verified exact1
verified fuzzy18
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cb0b9b4d-dac7-4c85-aef1-ec8994ae7c37 · outbound

This paper cites A survey of modern deep learning based object detection models,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers A survey of modern deep learning based object detection models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.802812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeeddf61-a801-4cb6-a239-da7a68dd1d8a · outbound

This paper cites A survey: object detection methods from cnn to transformer,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers A survey: object detection methods from cnn to transformer,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.184851Z

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 de40e37e-0a84-416a-b2c0-f03a22617e22 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers End-to-end object detection with transformers,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.809125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab4d05f6-4a47-48ca-bc0b-c9901bb4a7a1 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.812442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba459870-ec33-4da8-a043-cce0157967f4 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.817063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3da679-a7f8-4fe4-b666-3517224b9354 · outbound

This paper cites Evaluating the adversarial robustness of detection transformers,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Evaluating the adversarial robustness of detection transformers,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.171633Z

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 047b7e77-d803-4eb6-817a-1e7ac126e897 · outbound

This paper cites Adversarial attention perturbations for large object detection transformers,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Adversarial attention perturbations for large object detection transformers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.164041Z

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 dea52e30-93ba-4703-98d6-6c5af47a1ca0 · outbound

This paper cites Lite detr: An interleaved multi-scale encoder for efficient detr,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Lite detr: An interleaved multi-scale encoder for efficient detr,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.155102Z

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 ad484536-eb0d-44bd-b40e-46dc0316c947 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Give me your attention: Dot-product attention considered harmful for adversarial patch robustness,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.146811Z

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 89c56a5b-918b-40c6-b59f-b49749706a81 · outbound

This paper cites Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.832506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a1edbbd-3136-4df8-aa5d-bd9a4a5cdd06 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Adversarial examples for semantic segmentation and object detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.138986Z

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 538ec394-0bf5-4507-b41b-de297eaca26e · outbound

This paper cites Transferable Adversarial Attacks for Image and Video Object Detection.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Transferable Adversarial Attacks for Image and Video Object Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.838424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3af7cc6b-d1c7-44d0-9a68-a3a9bd8c272a · outbound

This paper cites Robust Adversarial Perturbation on Deep Proposal-based Models.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Robust Adversarial Perturbation on Deep Proposal-based Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.842568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a6962a0-141a-4ef8-98a5-973fafc60baa · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Adversarial objectness gradient attacks in real-time object detection systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.130282Z

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 bd6fc859-4134-4356-8945-661a6559995b · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Explaining and Harnessing Adversarial Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.848186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45240bc9-5937-4cc3-a52a-34c3adec218e · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Towards deep learning models resistant to adversarial attacks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.122031Z

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 986df3e9-8503-4883-b9f4-9b9eec4e69a3 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Towards evaluating the robustness of neural networks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.854213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59e12877-6905-4cdb-a9a8-909a90ad306d · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.857020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba27241-4bd3-4020-9e2c-3120804b3de1 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Training data-efficient image transformers & distillation through attention,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.859942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7a0c03b-e3ac-46e6-b868-bfd05a87b2ab · outbound

This paper cites Localized query attack toward transformer-based visible object detectors,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Localized query attack toward transformer-based visible object detectors,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.105643Z

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 eecb2152-75d8-4913-87a7-97fed269214a · outbound

This paper cites Ad- versarial attention deficit: Fooling deformable vision transformers with collaborative adversarial patches,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Ad- versarial attention deficit: Fooling deformable vision transformers with collaborative adversarial patches,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.096419Z

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 2deb2707-32dd-4b3f-8809-4a2bd689f766 · outbound

This paper cites The lipschitz constant of self- attention,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers The lipschitz constant of self- attention,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.088090Z

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 571aa0f0-4369-4236-a45a-acf4432815e1 · outbound

This paper cites Attention is all you need,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Attention is all you need,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.875645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae92e26f-41f5-438e-8b82-4af2367ddeb8 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Microsoft coco: Common objects in context,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.878857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfbc8305-d812-4ec8-8ad3-5af93bfbc3d6 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Pytorch: An imperative style, high-performance deep learning library,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.881469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3c7ec68-418e-44fa-a5f7-9232368c6e49 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers A large-scale multiple-objective method for black-box attack against object detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.066266Z

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 a35bed58-a041-42e7-a314-f355e43c95dc · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Improving transferable adversarial attack for vision transformers via global attention and local drop,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.057788Z

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 94a4d4f2-4da7-4722-b633-b1aa3e57da4e · outbound

This paper cites Relevance Attack on Detectors.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Relevance Attack on Detectors

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:46:06.939996Z

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 e145be79-6761-4890-8bd1-77e13599c1fe · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Gradient- guided hierarchical feature attack for object detector,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.049432Z

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 40068108-d616-42e0-946f-97a8d5f4d7b1 · outbound

This paper cites Ensemble-based blackbox attacks on dense prediction,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Ensemble-based blackbox attacks on dense prediction,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.039734Z

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 af4b86ac-ae67-4188-b640-84e97e158e74 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Object-aware transfer-based black-box adversarial attack on object detector,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.031047Z

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 fb487fc1-44bf-4d32-97d7-4a4ba26abb3e · outbound

This paper cites Deep residual learning for image recognition,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Deep residual learning for image recognition,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.901676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c31c02ad-9d25-411c-8d9f-987961647a41 · outbound

This paper cites A convnet for the 2020s,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers A convnet for the 2020s,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.904284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d57557-cb15-4554-8909-54d48162d6f6 · outbound

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

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.906852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6986e513-9ab0-49d2-a0db-d987acd87d29 · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Internimage: Exploring large-scale vision foundation models with deformable convolutions,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:07.009386Z

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 0c0e8b4e-5ac2-4d4e-ad0a-d71188eda6fa · outbound

This paper cites Focal modulation networks,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Focal modulation networks,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:06.911998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff466371-215b-4221-bb6f-e6e59ad05d31 · outbound

This paper cites Eva: Exploring the limits of masked visual representation learning at scale,.

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers Eva: Exploring the limits of masked visual representation learning at scale,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:06.995733Z

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

No inbound Pith citation observations are available.