Paper Citation Record · LEDGER

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.15636.

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

pith.paper-citation-record.v1
2507.15636 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:32:01.601958Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy23
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a627ab2-c0ba-424a-bddd-3645a9e6cff2 · outbound

This paper cites Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.908895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c61e7d4b-21ef-4892-ac67-5b51abf35dea · outbound

This paper cites Generative AI and deep fakes in media industry– An innovation resistance theory perspective,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Generative AI and deep fakes in media industry– An innovation resistance theory perspective,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.797804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7cb956ca-c71f-4f49-97ce-5519569ea86b · outbound

This paper cites A deepfake compressed video detection method based on dense dynamic CNN,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis A deepfake compressed video detection method based on dense dynamic CNN,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.683738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5cebc89b-55d0-425f-8aa6-6ee15cae7dec · outbound

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

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Detecting compressed deep- fake videos in social networks using frame-temporality two-stream convolutional network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.548324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1809df61-604a-4abe-9015-57265a979d62 · outbound

This paper cites A lightweight CNN for efficient deepfake detection of low-resolution images in frequency domain,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis A lightweight CNN for efficient deepfake detection of low-resolution images in frequency domain,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.389483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 80b6a7e7-ccf9-4ca1-9c1e-24258a8d0e00 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.243019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 24efcbe2-929e-4c9f-8322-6a6f42ee5821 · outbound

This paper cites Exploiting deepfakes by analyzing temporal feature inconsistency.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Exploiting deepfakes by analyzing temporal feature inconsistency

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:04.087015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0cd46256-eb45-4149-9950-fa4e13402b5f · outbound

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

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis MesoNet: a compact facial video forgery detection network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.960847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 859a403d-18c2-41b0-9216-a66f5cf0621b · outbound

This paper cites Deep residual learning for image recognition,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Deep residual learning for image recognition,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:00.128614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 468a7351-3777-4386-bf42-6f017d25903f · outbound

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

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:00.236836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00b4997a-a6e1-4762-80d2-494aa93968ef · outbound

This paper cites The open images dataset v4: Unified image classi- fication, object detection, and visual relationship detection at scale,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis The open images dataset v4: Unified image classi- fication, object detection, and visual relationship detection at scale,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.844398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cc3c9905-320e-4761-9111-8b266177d6de · outbound

This paper cites FaceForensics++: Learning to detect manipulated facial images,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis FaceForensics++: Learning to detect manipulated facial images,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.660753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3bcca0f0-7892-46ad-9e94-939ec8151b04 · outbound

This paper cites Fake- buster: A lightweight solution for deepfake detection,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Fake- buster: A lightweight solution for deepfake detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.491527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3faac1e1-c845-4565-ba81-c65d9ed92c51 · outbound

This paper cites Neural geometric level of detail: Real-time rendering with implicit 3D shapes,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Neural geometric level of detail: Real-time rendering with implicit 3D shapes,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.393538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 84bfad1d-de63-4f98-aacb-bcfe6fee6231 · outbound

This paper cites LLM-Pruner: On the structural pruning of large language models,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis LLM-Pruner: On the structural pruning of large language models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.218202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 080eb23b-fa59-49a1-98ed-9978e9002d78 · outbound

This paper cites Pruning for robust concept erasing in diffusion models,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Pruning for robust concept erasing in diffusion models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:03.032096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d04ad098-35cc-489b-b92c-d030d54ec4d4 · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Pruning convolutional neural networks for resource efficient inference,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.914403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b15dc78e-0502-45cf-ba52-cdc174acc5a1 · outbound

This paper cites RTMobile: Beyond real-time mobile acceleration of rnns for speech recognition,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis RTMobile: Beyond real-time mobile acceleration of rnns for speech recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.752204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb244ace-bc73-4883-816f-d8dcf90d2c0e · outbound

This paper cites 3D point cloud network pruning: When some weights do not matter,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis 3D point cloud network pruning: When some weights do not matter,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.642026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 38b89041-da95-476e-a667-b95708aef026 · outbound

This paper cites Deepfake video detection: challenges and opportunities,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Deepfake video detection: challenges and opportunities,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:01.035139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 771deed6-1eea-4371-abac-05632ab067b9 · outbound

This paper cites What Do Compressed Deep Neural Networks Forget?.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis What Do Compressed Deep Neural Networks Forget?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:01.096012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52fff454-fa17-4db3-95f3-e2c43f51f90e · outbound

This paper cites Grad-CAM: Visual explanations from deep networks via gradient-based localization,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Grad-CAM: Visual explanations from deep networks via gradient-based localization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.520512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c35e836d-41d1-472d-a745-c801438f01ed · outbound

This paper cites Adaptive knowledge dis- tillation for classification of hand images using explainable vision transformers,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Adaptive knowledge dis- tillation for classification of hand images using explainable vision transformers,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.376314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70209da8-d5c3-4b37-9ced-843daadd75ab · outbound

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

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Celeb-DF: A large- scale challenging dataset for deepfake forensics,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.266253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a8d75596-d751-45bb-9b7c-b689c9dbaa99 · outbound

This paper cites Stabilizing the Lottery Ticket Hypothesis.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Stabilizing the Lottery Ticket Hypothesis

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:01.367340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88ff770c-9ecf-4b04-8555-444ac7af7d97 · outbound

This paper cites Coarsening the granularity: Towards structurally sparse lottery tickets,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Coarsening the granularity: Towards structurally sparse lottery tickets,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.162514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 970327b6-55cf-4069-acbc-0b3116206d9d · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:01.547904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba5f4a3b-cf98-4f4c-89be-0a37fcf2642a · outbound

This paper cites Picking winning tickets before training by preserving gradient flow,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Picking winning tickets before training by preserving gradient flow,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:02.011067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c2fc86e9-9f9a-49aa-b21d-b8900aee8b14 · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representations,.

Uncovering Critical Features for Deepfake Detection through the Lottery Ticket Hypothesis Shallowing deep networks: Layer-wise pruning based on feature representations,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:01.874450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.