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Paper Citation Record · LEDGER

Exploring Spatial-Temporal Features for Deepfake Detection and Localization

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.15872.

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

pith.paper-citation-record.v1
2210.15872 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:52:38.875375Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T23:25:47.807721Z

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 92f6a4a3-2737-4556-a9cd-7ba426747a32 · inbound

Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection cites this paper.

Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection Exploring Spatial-Temporal Features for Deepfake Detection and Localization

Reference 288

Resolution
verified exact
arxiv_id, observed 2026-05-17T23:25:47.809822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-17T23:25:47.549655Z digest=sha256:d1827c843fec97593b7253774f97c45ae9e3eea1c89b235ad41121b63fe055ce

Observation 3f5754d4-f325-4a8b-beb9-06da0e54da2f · inbound

Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection cites this paper.

Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection Exploring Spatial-Temporal Features for Deepfake Detection and Localization

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T15:52:38.875375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:52:38.875375Z digest=sha256:a43f55b9197e75ca7348bba73c26fe75a281ee96e4f3cf663fe1b8633c3e7495