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Paper Citation Record · LEDGER

An Examination of Fairness of AI Models for Deepfake Detection

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2105.00558.

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

pith.paper-citation-record.v1
2105.00558 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 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:17:58.754338Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 03d61a64-27d0-4be6-a7ac-d40a4e076fb7 · inbound

Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising cites this paper.

Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising An Examination of Fairness of AI Models for Deepfake Detection

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T18:17:58.754338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:17:58.754338Z digest=sha256:b551453da174e848a94397f92ed67da20ae949b90742600c9fae01656b44b58e

Observation 6a0fd97e-3222-460a-973c-e4b9d8a044d0 · inbound

Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis cites this paper.

Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis An Examination of Fairness of AI Models for Deepfake Detection

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:55:37.869257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T12:55:19.270661Z digest=sha256:9c2e88b70973b7d20452f4519b57e693abadb80bd68cad1fbaac9ddae0f8fdad

Observation 12213498-e2f6-4acd-be6b-acb87fa0e02f · inbound

Dual-Use AI Face Swap Apps Are Mostly Unsafe: A Systematic Safety Audit cites this paper.

Dual-Use AI Face Swap Apps Are Mostly Unsafe: A Systematic Safety Audit An Examination of Fairness of AI Models for Deepfake Detection

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-06-30T11:54:38.295853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T11:50:48.983907Z digest=sha256:0398f8d30c7a11048f50761b12daa96d1d757e0418160e172f13d0be47ecb710

Observation c7307f7e-41a7-48a4-95b5-1c66ea390437 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection An Examination of Fairness of AI Models for Deepfake Detection

Reference 117

Resolution
verified exact
arxiv_id, observed 2026-06-28T07:11:45.032721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T07:05:18.026601Z digest=sha256:f2a5bee93278494cbb6f2b4eb042bdc3a9417573bcf91a7f935748311bd0804e

Observation 74a5b503-d539-4f04-b7f4-3a33ee616212 · inbound

What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection cites this paper.

What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection An Examination of Fairness of AI Models for Deepfake Detection

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-14T14:47:58.592181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T14:47:58.592181Z digest=sha256:4f5b2ce61b19c013a49da12a2de3e61ecff58c2a443a4e3be7e5431b424ddb21