Paper Citation Record · LEDGER

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT

As of 23 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.07922.

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

pith.paper-citation-record.v1
2607.07922 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T15:22:10.143336Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 8d1a9d5c-2463-4f28-bd14-8943eba69c58 · outbound

This paper cites Quantifying atten- tion flow in transformers.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Quantifying atten- tion flow in transformers

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.424705Z

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Observation e9682c58-0f3d-402a-bdc4-5761a04a0a42 · outbound

This paper cites Reveal of Vision Transformers Robustness against Adversarial Attacks.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Reveal of Vision Transformers Robustness against Adversarial Attacks

Reference 2

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Observation 425b6722-8c34-43a1-82ef-756de5da4623 · outbound

This paper cites Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

Reference 3

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Observation d2b5d8fa-c979-4e4b-b7f5-e13f2d65c8f0 · outbound

This paper cites Under- standing robustness of transformers for image classification.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Under- standing robustness of transformers for image classification

Reference 4

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Observation d8c7633f-37e3-4426-bb7a-a3917648b4ae · outbound

This paper cites Adversarial Patch.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Adversarial Patch

Reference 5

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Source-reported events for the cited work

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Observation 1fcab95a-32bc-4bd3-8481-540ff7b421e9 · outbound

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

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT End-to- end object detection with transformers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.419197Z

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Observation de1e4036-1736-4608-900d-a2ff539a9865 · outbound

This paper cites Transformer inter- pretability beyond attention visualization.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Transformer inter- pretability beyond attention visualization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.417493Z

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Observation 0483a99a-67af-428a-b2b3-753cc44ac255 · outbound

This paper cites Vision transformers need registers.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Vision transformers need registers

Reference 8

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Observation d7bc2a45-8bd8-40ed-a02a-8109befa1319 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.412047Z

Source-reported events for the cited work

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Observation 9c7f05e7-8f98-4765-b2a9-7412772b557f · outbound

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

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-10T15:27:20.176380Z

Source-reported events for the cited work

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Observation 54aa7663-d460-4cc9-b3e2-e75313e9c4cc · outbound

This paper cites Pruning one more token is enough: Leveraging latency- workload non-linearities for vision transformers on the edge.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Pruning one more token is enough: Leveraging latency- workload non-linearities for vision transformers on the edge

Reference 11

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Observation de4d9fef-3609-41c9-8c87-965a590ddc4e · outbound

This paper cites Patch-fool: Are vision transformers always robust against adversarial perturbations? InTenth In- ternational Conference on Learning Representations (ICLR 2022), 2022.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Patch-fool: Are vision transformers always robust against adversarial perturbations? InTenth In- ternational Conference on Learning Representations (ICLR 2022), 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.428533Z

Source-reported events for the cited work

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Observation fbfc4c1a-0b07-411c-80f7-49774fef8d7b · outbound

This paper cites Are vision trans- formers robust to patch perturbations? InEuropean Con- ference on Computer Vision, pages 404–421.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Are vision trans- formers robust to patch perturbations? InEuropean Con- ference on Computer Vision, pages 404–421

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.433917Z

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Observation 7f69509a-526b-41eb-82b3-3a69e9edb973 · outbound

This paper cites Vision transformers don’t need trained regis- ters.Advances in neural information processing systems, 38: 56557–56595, 2026.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Vision transformers don’t need trained regis- ters.Advances in neural information processing systems, 38: 56557–56595, 2026

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.406599Z

Source-reported events for the cited work

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Observation 0e6ab187-ad9a-4935-9d74-2e1e1772a66f · outbound

This paper cites Seeing isn’t believing: Context-aware adversarial patch synthesis via conditional gan.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Seeing isn’t believing: Context-aware adversarial patch synthesis via conditional gan

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.415581Z

Source-reported events for the cited work

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Observation 5d813680-2fdf-4db5-be28-831bfa53f774 · outbound

This paper cites AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-10T15:27:20.178950Z

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Observation baeab121-b174-47b5-81c5-8d0f0170d1e1 · outbound

This paper cites Under- standing and defending patched-based adversarial attacks for vision transformer.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Under- standing and defending patched-based adversarial attacks for vision transformer

Reference 17

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Observation e2b32caa-1369-42d3-a340-e00dcde33fcb · outbound

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

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Give me your attention: Dot-product attention considered harmful for adversarial patch robustness

Reference 18

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Observation 7a727965-f7c9-49c9-ba4e-18497d3c42da · outbound

This paper cites Understanding the effective receptive field in deep convolu- tional neural networks.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Understanding the effective receptive field in deep convolu- tional neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.404807Z

Source-reported events for the cited work

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Observation 17cbabe0-cc11-4de9-8d9e-30dfe8263679 · outbound

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

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Towards deep learn- ing models resistant to adversarial attacks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.403058Z

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Observation dac19eb4-6100-4081-b757-9e6f7d8fc00c · outbound

This paper cites Defending against adversar- ial patches with robust self-attention.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Defending against adversar- ial patches with robust self-attention

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.408339Z

Source-reported events for the cited work

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Observation f6617190-1743-4c2d-aa70-3a1b0a343fad · outbound

This paper cites Intriguing properties of vision transform- ers.Advances in Neural Information Processing Systems, 34: 23296–23308, 2021.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Intriguing properties of vision transform- ers.Advances in Neural Information Processing Systems, 34: 23296–23308, 2021

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.399568Z

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Observation d79a12c0-2131-476a-9648-153abb7e7991 · outbound

This paper cites Dinov2: Learning robust visual features without supervision.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Dinov2: Learning robust visual features without supervision

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.413805Z

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Observation 1663c546-5666-49ef-8a0d-a7a47ef5e6df · outbound

This paper cites Vision transformers are robust learners.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Vision transformers are robust learners

Reference 24

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Observation e2f31529-108e-46fb-a413-cafd6182152e · outbound

This paper cites Benchmarking the spatial ro- bustness of dnns via natural and adversarial localized corrup- tions.Pattern Recognition, page 112412, 2025.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Benchmarking the spatial ro- bustness of dnns via natural and adversarial localized corrup- tions.Pattern Recognition, page 112412, 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.397584Z

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Observation 7932d79b-edef-4e5f-9dd8-81ab8da5ccb8 · outbound

This paper cites Defending from physically- realizable adversarial attacks through internal over-activation 9 analysis.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Defending from physically- realizable adversarial attacks through internal over-activation 9 analysis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.392235Z

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Observation 5ded3ad5-8e1f-4480-b47f-5934d82078c7 · outbound

This paper cites On the adversarial robustness of vision trans- formers.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT On the adversarial robustness of vision trans- formers

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.390556Z

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Observation 08074ba9-da57-456c-aea4-ec43fb1155e5 · outbound

This paper cites Attacking attention of foundation models disrupts downstream tasks.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Attacking attention of foundation models disrupts downstream tasks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.401341Z

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Observation a1ee95f1-37b0-4c64-8ea9-b805b4a18ebb · outbound

This paper cites Localizing objects with self-supervised transformers and no labels.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Localizing objects with self-supervised transformers and no labels

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.388832Z

Source-reported events for the cited work

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Observation 32f650f9-7add-43d0-a89b-1b4c5d18c3d6 · outbound

This paper cites Segmenter: Transformer for semantic segmenta- tion.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Segmenter: Transformer for semantic segmenta- tion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.426428Z

Source-reported events for the cited work

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Observation 38969bc3-4d76-4d72-aee0-72e803af1f37 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Training data-efficient image transformers & distillation through at- tention

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.386967Z

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This paper cites Attention is all you need.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Attention is all you need

Reference 32

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Source-reported events for the cited work

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Observation 075c3767-3c98-4186-bcd4-9b88c70c2bfd · outbound

This paper cites Patch ranking: Token pruning as ranking prediction for efficient clip.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Patch ranking: Token pruning as ranking prediction for efficient clip

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.410280Z

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Observation 919b240b-068d-4f8f-aaea-75f14d8c09a8 · outbound

This paper cites Adversarial t-shirt! evading person detectors in a physical world.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Adversarial t-shirt! evading person detectors in a physical world

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T15:27:20.385186Z

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Observation 6f9ceb83-ff91-4379-9f0a-667c2b174746 · outbound

This paper cites Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT.

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT

Reference 35

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Source-reported events for the cited work

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Pith citing papers

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