Paper Citation Record · LEDGER

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2501.13435.

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

pith.paper-citation-record.v1
2501.13435 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:11:51.899245Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T09:16:37.881212Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb650544-3f6c-4e2d-8f12-9c2ba0886153 · outbound

This paper cites Copy Motion From One to Another: Fake Motion Video Generation.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Copy Motion From One to Another: Fake Motion Video Generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T16:11:51.819367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5da031c-b3aa-4519-969c-03f980ae8bbd · outbound

This paper cites Deepfakes: perspec- tives on the future “reality.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Deepfakes: perspec- tives on the future “reality

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.171989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41559f78-9c0e-4944-b8d9-20d452dd25d1 · outbound

This paper cites Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolutional network,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolutional network,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.159135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c3ceda49-386a-4eb9-8dd4-67be97414f05 · outbound

This paper cites Capsule- forensics: Using capsule networks to detect forged images and videos,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Capsule- forensics: Using capsule networks to detect forged images and videos,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.146716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4460d715-cfc0-4d37-b885-1bbd364caa1e · outbound

This paper cites Mesonet: a compact facial video forgery detection network,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Mesonet: a compact facial video forgery detection network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.135450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 84d50538-4a91-48da-9d6d-0c73dd35bb8f · outbound

This paper cites Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.125006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a483e911-eda3-4346-9023-c2969445fd89 · outbound

This paper cites Deepfake video detection using recurrent neural networks,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Deepfake video detection using recurrent neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.113014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 359432f9-c537-4c67-b0fb-11862765eadb · outbound

This paper cites In ictu oculi: Exposing ai created fake videos by detecting eye blinking,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection In ictu oculi: Exposing ai created fake videos by detecting eye blinking,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.100629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87adc4c6-7d9f-4d78-8819-f6307ad13060 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Xception: Deep learning with depthwise separable convolutions,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.088963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc675d19-3ad9-4380-a4b4-8b4f3a0d5d5a · outbound

This paper cites Flownet: Learning optical flow with convolutional networks,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Flownet: Learning optical flow with convolutional networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.077461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1774716b-cab2-49fc-b8a9-dd2e15d72abb · outbound

This paper cites Masked feature prediction for self-supervised visual pre-training,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Masked feature prediction for self-supervised visual pre-training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.066378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a7456529-d530-42e6-bb7d-9808898f4bdd · outbound

This paper cites FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T16:11:51.863719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc5d06ad-6f3b-4fb2-8f2d-6e2ad110f2ac · outbound

This paper cites Celeb-df: A large-scale challenging dataset for deepfake forensics,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Celeb-df: A large-scale challenging dataset for deepfake forensics,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.055942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11d93ad9-7112-4d73-9319-74b2a9c39dbd · outbound

This paper cites Temporal surface frame anomalies for deepfake video detection,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Temporal surface frame anomalies for deepfake video detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.044455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bde2405d-cd25-4357-93a5-90793bb1694c · outbound

This paper cites Deepfake videos detection via spatiotemporal inconsistency learning and interactive fu- sion,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Deepfake videos detection via spatiotemporal inconsistency learning and interactive fu- sion,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.033658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 17ca913a-2303-47e3-9c5e-0c423e574875 · outbound

This paper cites Learning spatiotemporal features with 3d convolu- tional networks,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Learning spatiotemporal features with 3d convolu- tional networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.022431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8cd452f-086b-4e0b-bee6-52210b5d523d · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:52.009760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 087347ea-48be-4f5a-9b60-15a4b95c542d · outbound

This paper cites Wilddeepfake: A challenging real-world dataset for deepfake detection,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Wilddeepfake: A challenging real-world dataset for deepfake detection,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:51.998680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8a6d3b06-ca7e-422a-add5-79ccfdf7a5e1 · outbound

This paper cites Msvt: Multiple spatiotemporal views transformer for deepfake video detection,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Msvt: Multiple spatiotemporal views transformer for deepfake video detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:51.987494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 953ef94b-6e75-4ba5-8259-1e40cf16789f · outbound

This paper cites Famm: facial muscle motions for detecting compressed deepfake videos over social networks,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Famm: facial muscle motions for detecting compressed deepfake videos over social networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:51.976629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee52a7b3-3b44-4336-a4a0-ba5c5749599e · outbound

This paper cites Dynamic difference learning with spatio-temporal correlation for deepfake video detection,.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Dynamic difference learning with spatio-temporal correlation for deepfake video detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:11:51.963912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc774a44-1770-415b-b112-5fa0b5eeec0f · outbound

This paper cites Exposing Lip-syncing Deepfakes from Mouth Inconsistencies.

GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection Exposing Lip-syncing Deepfakes from Mouth Inconsistencies

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T16:11:51.899245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 497d868e-4bbb-4005-ab72-563229e7037b · inbound

Detecting AI-Generated Video: A Vision-Language Dual-View Survey cites this paper.

Detecting AI-Generated Video: A Vision-Language Dual-View Survey GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-14T09:16:37.881212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.