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Paper Citation Record · LEDGER

Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.06408.

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

pith.paper-citation-record.v1
2304.06408 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T01:08:22.794058Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:45:48.486161Z

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 58b05fa2-77c8-471b-8ce9-8cd369058e7f · 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? Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:34:26.611487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T23:31:40.691896Z digest=sha256:6317ca2f0c6bb2f8ed9816de43ed9a636d15d760f3297272c81f286bb54cd130

Observation 86a1e2f6-0238-432c-966d-6df55d211fa7 · inbound

Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images cites this paper.

Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-01T15:45:48.487556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T01:08:22.794058Z digest=sha256:807ca6320f7fe35199f0e30ad1cafbaa5c085e3e6d82343862f3cd552b5ba0a1