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Paper Citation Record · LEDGER

On the detection of synthetic images generated by diffusion models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2211.00680.

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

pith.paper-citation-record.v1
2211.00680 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T08:02:37.135363Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:59:07.398766Z

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 5d20b508-6eec-4574-ad5c-2894c488f0de · inbound

Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters? cites this paper.

Navigating the Challenges of AI-Generated Image Detection in the Wild: What Truly Matters? On the detection of synthetic images generated by diffusion models

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:34:26.592621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-21T23:31:40.691896Z digest=sha256:562d2d6b6e7391394a2bae624bba6e3f9ac37e0840fc96d86789bb0e12affab6

Observation fe64f637-d2b8-4e9c-819b-562fae8a4a0e · inbound

Deepfake Detection Generalization with Diffusion Noise cites this paper.

Deepfake Detection Generalization with Diffusion Noise On the detection of synthetic images generated by diffusion models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:25:19.607868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T11:22:31.988852Z digest=sha256:f9cf30e373bb107d6519ca2283b09964dd128ab33c0b424f9d54b99f45452870

Observation 8254fae0-9fc6-444b-a52b-ee497040da72 · inbound

The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence cites this paper.

The CIFAR Synthetic Evidence Corpus for Detecting AI-Generated Evidence On the detection of synthetic images generated by diffusion models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:47:22.757504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-27T20:12:40.026108Z digest=sha256:3a3ddb1a3c558e24bb0377db54890c472a70b23011976b48131a92bc73e006fd

Observation fc06718e-2411-47cd-a025-090f775153c8 · inbound

Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion cites this paper.

Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion On the detection of synthetic images generated by diffusion models

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:59:07.403772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T21:32:27.296146Z digest=sha256:9a824ec75c19a4c47c68f96cb59bb51132d9b4e65c3846f94e8abfa2c3586acc

Observation 934a6340-cfe8-4003-b59b-bce6c5beea40 · inbound

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models cites this paper.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models On the detection of synthetic images generated by diffusion models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-12T08:02:37.135363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:6aee78c927c9815e382fb17481cb5e9bbe51cb6db6f0595a44c44122d0928bd1