Paper Citation Record · LEDGER

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization

As of 20 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2505.19613.

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

pith.paper-citation-record.v1
2505.19613 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:16:27.294582Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T01:25:06.528845Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T01:25:52.696115Z

Reference resolution

60 of 60 outbound references displayed

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External citation measurements

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Outbound references

Observation 8674c74c-2692-43b9-9e95-f0908281cb3b · outbound

This paper cites Quantifying Attention Flow in Transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Quantifying Attention Flow in Transformers

Reference 1

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Observation ca97d104-602b-4bee-b39e-b56b931bdb37 · outbound

This paper cites Understanding robustness of transformers for image classification.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Understanding robustness of transformers for image classification

Reference 2

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Observation d1b2a8c4-45eb-4463-a1af-f63eecc55a3b · outbound

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

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization End-to-end object detection with transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:20.568019Z

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Observation a9411de4-3359-4131-80c1-efcd68a7a319 · outbound

This paper cites Visformer: The vision-friendly transformer.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Visformer: The vision-friendly transformer

Reference 4

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Observation be34b085-417d-44d9-aeea-11833d22e6f1 · outbound

This paper cites A light recipe to train robust vision transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization A light recipe to train robust vision transformers

Reference 5

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Observation d81c3925-11b5-4482-824a-d52a25b89a9f · outbound

This paper cites Boosting adversarial attacks with momentum.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Boosting adversarial attacks with momentum

Reference 6

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Observation e29bd998-791e-4db9-9b6c-8bd77c23bc46 · outbound

This paper cites Boosting adversarial attacks with momentum.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Boosting adversarial attacks with momentum

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:33.797096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 837ca276-691b-43cb-8afd-42f523be1f31 · outbound

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

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation c131479c-8bc4-46e2-b138-b7af6cd30238 · outbound

This paper cites Convit: Improving vision transformers with soft convolutional inductive biases.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Convit: Improving vision transformers with soft convolutional inductive biases

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:33.599657Z

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Observation 11a8d691-ad63-4446-ae0a-8344524db841 · outbound

This paper cites Fda: Feature disruptive attack.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Fda: Feature disruptive attack

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:33.438933Z

Source-reported events for the cited work

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Observation 5f96ff8a-8f1d-42c2-adf0-e0eee159f9c0 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Explaining and Harnessing Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:21.464580Z

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Observation 54cff937-a08f-47a9-a2ac-f1c81570bbf8 · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Levit: a vision transformer in convnet’s clothing for faster inference

Reference 13

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Observation dcf4f51d-76d7-46f8-96b4-023076f795e8 · outbound

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TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 14

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Observation 3028a257-5286-4c5e-a891-1273cb1f7622 · outbound

This paper cites Dap: A dynamic adversarial patch for evading person detectors.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Dap: A dynamic adversarial patch for evading person detectors

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:33.283770Z

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Observation 93531995-8677-4330-ba1c-4ea94826566b · outbound

This paper cites Ssap: A shape-sensitive adversarial patch for comprehensive disruption of monocular depth estimation in autonomous navigation applications.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Ssap: A shape-sensitive adversarial patch for comprehensive disruption of monocular depth estimation in autonomous navigation applications

Reference 16

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Observation 412d6102-e796-43bf-9a16-7505fdae029a · outbound

This paper cites Transformer in transformer.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Transformer in transformer

Reference 17

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Observation 44286c68-284c-47ef-afd0-f7e09f4e8bf7 · outbound

This paper cites Deep residual learning for image recognition.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Deep residual learning for image recognition

Reference 18

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Observation c525d26e-f874-4cf5-8d89-1cd0f1495ad3 · outbound

This paper cites Rethinking spatial dimensions of vision transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Rethinking spatial dimensions of vision transformers

Reference 19

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Observation e83ffd02-6127-4dd6-935f-75d76e16fc70 · outbound

This paper cites Enhancing adversarial example transferability with an intermediate level attack.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Enhancing adversarial example transferability with an intermediate level attack

Reference 20

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Observation 0350c927-575e-4beb-8879-0723b0e4b3c4 · outbound

This paper cites Exploring adversarial robustness of vision transformers in the spectral perspective.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Exploring adversarial robustness of vision transformers in the spectral perspective

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:33.099870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68d03629-c23a-4ad1-85ad-b67529024ec4 · outbound

This paper cites Attention is Not Only a Weight: Analyzing Transformers with Vector Norms.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Attention is Not Only a Weight: Analyzing Transformers with Vector Norms

Reference 22

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Observation 1c470977-c747-46aa-9cfa-3b216a7a2a90 · outbound

This paper cites Leveraging visual question answering for image-caption ranking.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Leveraging visual question answering for image-caption ranking

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.949541Z

Source-reported events for the cited work

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Observation c704b85c-a453-4d30-af5d-3a3d223adc59 · outbound

This paper cites Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Beyond attentive tokens: Incorporating token importance and diversity for efficient vision transformers

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.772680Z

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Observation 073efa48-615f-43f0-b4be-510b8d6a1093 · outbound

This paper cites Unified transformer tracker for object tracking.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unified transformer tracker for object tracking

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.593408Z

Source-reported events for the cited work

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Observation 30058c2a-df09-441a-83e9-d3ec05293856 · outbound

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

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 26

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Observation f3c5e014-05bd-4ed7-bf38-8a1de202fd49 · outbound

This paper cites Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning.Advances in Neural Information Processing Systems, 37: 20887–20918, 2024.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning.Advances in Neural Information Processing Systems, 37: 20887–20918, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.436850Z

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Observation 0600beba-5f9f-4cef-b567-fed0fd165afe · outbound

This paper cites When adversarial training meets vision transformers: Recipes from training to architecture.Advances in Neural Information Processing Systems, 35:18599–18611, 2022.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization When adversarial training meets vision transformers: Recipes from training to architecture.Advances in Neural Information Processing Systems, 35:18599–18611, 2022

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.261433Z

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Observation 7ba69767-aa0e-462d-94c0-6fed107a9135 · outbound

This paper cites GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers

Reference 30

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Observation 323d1daa-8c16-481c-a26a-5b1badc08008 · outbound

This paper cites On Improving Adversarial Transferability of Vision Transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization On Improving Adversarial Transferability of Vision Transformers

Reference 31

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Observation 40a080bc-4fce-43b7-ae70-852391353568 · outbound

This paper cites Do vision transformers see like convolutional neural networks?Advances in neural information processing systems, 34:12116–12128, 2021.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Do vision transformers see like convolutional neural networks?Advances in neural information processing systems, 34:12116–12128, 2021

Reference 32

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Observation 0020fa74-e6d8-4a98-ad4c-41621f38b26a · outbound

This paper cites Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:16:27.509495Z

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Observation 91aa761d-ce1c-4dc4-81b9-28c58d7a89ef · outbound

This paper cites Imagenet large scale visual recognition challenge.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Imagenet large scale visual recognition challenge

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:32.070825Z

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Observation ce085039-e393-4911-971d-66dd0e20df3a · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 35

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Observation 10f2f07b-c4d9-4c62-80fc-91d737c9faf8 · outbound

This paper cites Rethinking the inception architecture for computer vision.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Rethinking the inception architecture for computer vision

Reference 36

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Observation ae43bb10-c790-4341-b135-0ac3b33f8caf · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:31.907166Z

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Observation c922a8cb-eda4-4943-b07b-bb925fe12982 · outbound

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

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Training data-efficient image transformers & distillation through attention

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:24.766957Z

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Observation 9c444c9f-1933-47e3-aee1-b799a5287231 · outbound

This paper cites Going deeper with image transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Going deeper with image transformers

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:24.891651Z

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Observation f54e1a84-2f78-4a3b-937f-7dfb0b04ca0c · outbound

This paper cites Ro- bustness may be at odds with accuracy.arXiv: Machine Learning, 2018.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Ro- bustness may be at odds with accuracy.arXiv: Machine Learning, 2018

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:31.674470Z

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Observation dbdb5d37-9a0b-4cb2-849d-97b9515ccd4d · outbound

This paper cites Enhancing the transferability of adversarial attacks through variance tuning.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Enhancing the transferability of adversarial attacks through variance tuning

Reference 41

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Observation 363c505c-cf01-4b1c-9d77-b8f584ce5f8d · outbound

This paper cites Feature importance- aware transferable adversarial attacks.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Feature importance- aware transferable adversarial attacks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:31.466588Z

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Observation c5faa825-98e7-486d-97f3-d18972c2bdca · outbound

This paper cites Towards transferable adversarial attacks on vision transformers.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Towards transferable adversarial attacks on vision transformers

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:31.231944Z

Source-reported events for the cited work

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Observation 486e6fd3-37a1-47f8-9e32-84f965209477 · outbound

This paper cites Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:25.516863Z

Source-reported events for the cited work

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Observation c760c53e-dda0-428c-9a4d-200d28d13d4b · outbound

This paper cites Token transformation matters: Towards faithful post-hoc explanation for vision transformer.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Token transformation matters: Towards faithful post-hoc explanation for vision transformer

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:31.063957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4147fe32-7034-4d91-899b-5f803a9c7d3e · outbound

This paper cites Rethinking the backward propagation for adversarial transferability.Advances in Neural Information Processing Systems, 36:1905–1922, 2023.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Rethinking the backward propagation for adversarial transferability.Advances in Neural Information Processing Systems, 36:1905–1922, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:30.807638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 92562b92-e4b3-4004-a236-6ae61bbd8699 · outbound

This paper cites Improving transferability of adversarial examples with input diversity.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Improving transferability of adversarial examples with input diversity

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:25.809657Z

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Observation f41dbc59-55f5-4974-a0d1-cc73be66db4c · outbound

This paper cites Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Stochastic variance reduced ensemble adversarial attack for boosting the adversarial transferability

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:25.913030Z

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Observation cebcfefb-1907-4097-b2d5-99c1f0f8e32c · outbound

This paper cites A2: Efficient automated attacker for boosting adversarial training.Advances in Neural Information Processing Systems, 35:22844–22855, 2022.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization A2: Efficient automated attacker for boosting adversarial training.Advances in Neural Information Processing Systems, 35:22844–22855, 2022

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:30.602752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6694ee73-0439-4e9f-a997-7b6641e39c5a · outbound

This paper cites A fourier perspective on model robustness in computer vision.Advances in Neural Information Processing Systems, 32, 2019.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization A fourier perspective on model robustness in computer vision.Advances in Neural Information Processing Systems, 32, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:30.328159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a487940-9585-4bdd-8fe2-eddee5e5e67b · outbound

This paper cites How transferable are features in deep neural networks?Advances in neural information processing systems, 27, 2014.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization How transferable are features in deep neural networks?Advances in neural information processing systems, 27, 2014

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:26.113190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a99ce015-eb2f-49c3-93e2-6b6f78e8c191 · outbound

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

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Transferable adversarial attacks on vision transformers with token gradient regularization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:26.162055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2106c9be-c85f-4566-980a-6922dad4113e · outbound

This paper cites A unified efficient pyramid transformer for semantic segmentation.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization A unified efficient pyramid transformer for semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:30.052322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e14e3fe6-b16d-4965-a1ab-0e3f3dd10b4e · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:26.347939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1571c9fb-37af-43b3-978c-17206241cb8e · outbound

This paper cites This captures prior knowledge about the sensitivity of each module.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization This captures prior knowledge about the sensitivity of each module

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:29.856065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f67d287a-7ad8-4590-82d3-4d252310fd14 · outbound

This paper cites FSGS promotes perturbation alignment with semantically salient features while suppressing low-level, architecture-specific signals that degrade cross-model transferability.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization FSGS promotes perturbation alignment with semantically salient features while suppressing low-level, architecture-specific signals that degrade cross-model transferability

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:29.667544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e2ff7c1-7fa0-472b-bcf9-0bcea5eee3a1 · outbound

This paper cites an unresolved cited work.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:16:29.399872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 244f4933-b29f-405c-b067-ca769e0cb2e3 · outbound

This paper cites an unresolved cited work.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:16:29.145859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1455d1a-5cb9-4377-b2a0-40a95691ab04 · outbound

This paper cites , L}do foreachmodulem∈ {qkv,attn,mlp}do 3.1 Extract token features and gradients: Z(l,m) = [z(l,m) 1 ,.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization , L}do foreachmodulem∈ {qkv,attn,mlp}do 3.1 Extract token features and gradients: Z(l,m) = [z(l,m) 1 ,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:28.930917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fdf07850-3485-4310-ba71-3ba70ff92313 · outbound

This paper cites an unresolved cited work.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:16:28.746348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 54d2b860-0f41-4d7c-ada1-8082748ed94d · outbound

This paper cites an unresolved cited work.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:16:28.555637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17c04454-bada-4c2d-88eb-f35e699ac519 · outbound

This paper cites an unresolved cited work.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Unresolved cited work

Reference 63

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:16:28.408940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation a1982051-7868-4fb4-afcb-9d46c315bd37 · inbound

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs cites this paper.

Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T01:25:52.699619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.