Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.17911.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-15T02:38:37.358485Z
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
1
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 a60a99cb-4664-45ed-a6d4-f6eb6e23fc9d · inbound
SEED: A Large-Scale Benchmark for Provenance Tracing in Sequential Deepfake Facial Edits cites this paper.
SEED: A Large-Scale Benchmark for Provenance Tracing in Sequential Deepfake Facial Edits Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T16:15:34.470703Z
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.
Observation 01e361b8-e6cd-4e9d-a59d-ec74a9550715 · 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 Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:39:40.907359Z
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.