Paper Citation Record · LEDGER

SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2312.04913.

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

pith.paper-citation-record.v1
2312.04913 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:56:49.171617Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T00:25:48.524527Z

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 8d85a39e-df1e-4f18-bd37-d5cf99405b2d · inbound

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety cites this paper.

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 223

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:42:33.995280Z

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 24c044c8-0174-49a2-9003-9321ec9c3ba3 · inbound

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models cites this paper.

MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T10:56:49.171617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecdbbe7a-cca9-4aa1-9760-674bf2d4c7be · inbound

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment cites this paper.

Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:52.215725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94ac1984-3a7a-4856-a826-f280259693f9 · inbound

Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models cites this paper.

Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:35.832321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f999c7-dc3c-40cf-a482-00828550a3bc · inbound

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

Attacking Attention of Foundation Models Disrupts Downstream Tasks SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:09:06.917630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da6ce663-1381-4da2-a57b-edb21fe0b030 · inbound

Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP cites this paper.

Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T07:47:37.524533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd58d9e1-6764-4356-8264-85ca43fd7782 · inbound

Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers cites this paper.

Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T21:55:49.697599Z

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 bcd4b880-946a-4d83-b2e8-42ad63b05a3f · inbound

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models cites this paper.

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:23:21.524504Z

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 571cc4c3-5b68-4a4a-8560-666cf13937f5 · inbound

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective cites this paper.

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:25:48.525831Z

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 332058d4-c0bf-4d66-8adc-40c33ede1efd · inbound

GeoDetect: Geometric Adversarial Detection for VLPs cites this paper.

GeoDetect: Geometric Adversarial Detection for VLPs SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T01:17:29.774162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0d5434f-f3b0-44b2-b3f7-5fb29cc261ca · inbound

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline cites this paper.

On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T00:37:17.010873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.