Paper Citation Record · LEDGER

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2306.01902.

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

pith.paper-citation-record.v1
2306.01902 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:41:43.164852Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:07:56.125747Z

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 094d2314-3b83-4cdd-80bd-38f01036becd · inbound

Deepfake Detection Generalization with Diffusion Noise cites this paper.

Deepfake Detection Generalization with Diffusion Noise Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:25:19.675313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1f8718b1-d227-46f0-8a83-b3ff1723aac8 · inbound

To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model cites this paper.

To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:40.881741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfd6fc44-3e2e-48f2-a383-e2331b6d52dd · inbound

VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models cites this paper.

VOID: Defeating Unauthorized Mimicry in Latent Diffusion Models Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:07:56.127222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4bea95e0-510a-4adf-bdc1-0ad5673c14ea · inbound

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders cites this paper.

DiffUE: Enhancing Utility-Unlearnability Trade-off of Unlearnable Examples via Diffusion Autoencoders Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T10:41:43.164852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.