Paper Citation Record · LEDGER

On the Adversarial Robustness of Vision Transformers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2103.15670.

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

pith.paper-citation-record.v1
2103.15670 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:46.120059Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:12:35.065672Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 744b3f33-fe80-4b88-93dd-5f710e820754 · inbound

AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems cites this paper.

AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems On the Adversarial Robustness of Vision Transformers

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:46.120059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a32c214e-383b-4d0b-966d-24e3e07131ed · inbound

Attacking Attention of Foundation Models Disrupts Downstream Tasks cites this paper.

Attacking Attention of Foundation Models Disrupts Downstream Tasks On the Adversarial Robustness of Vision Transformers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:09.256860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 798d6395-ec77-44b7-b5e1-2407a90c3d84 · inbound

Are Fast Methods Stable in Adversarially Robust Transfer Learning? cites this paper.

Are Fast Methods Stable in Adversarially Robust Transfer Learning? On the Adversarial Robustness of Vision Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T22:07:02.997623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f16a6eeb-e01e-48a4-a872-ff716ebe783e · inbound

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack cites this paper.

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack On the Adversarial Robustness of Vision Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T16:25:08.955428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6354d186-d2ab-4d83-bd85-afcff013d420 · inbound

Benign Overfitting in Adversarial Training for Vision Transformers cites this paper.

Benign Overfitting in Adversarial Training for Vision Transformers On the Adversarial Robustness of Vision Transformers

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:18.225605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8c47f61-6014-4332-9ae5-75d05090edd1 · inbound

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models cites this paper.

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models On the Adversarial Robustness of Vision Transformers

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:33:37.939966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 103a0eae-f54a-469b-813f-4f7fa6348319 · inbound

SORA: Free Second-Order Attacks in Fast Adversarial Training cites this paper.

SORA: Free Second-Order Attacks in Fast Adversarial Training On the Adversarial Robustness of Vision Transformers

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:12:35.066913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.