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Paper Citation Record · LEDGER

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

As of 18 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2506.11477.

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

pith.paper-citation-record.v1
2506.11477 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:07:15.027658Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T13:08:29.414406Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:13:18.677212Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ed8e3af-d44c-4f03-be56-406231489a92 · outbound

This paper cites Mesonet: A com- pact facial video forgery detection network.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Mesonet: A com- pact facial video forgery detection network

Reference 1

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Observation 6800bfca-7078-4912-95fd-199e049244df · outbound

This paper cites Detecting deep-fake videos from aural and oral dy- namics.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Detecting deep-fake videos from aural and oral dy- namics

Reference 2

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Observation 94666255-b57f-4947-84e9-9ef54fc67e5c · outbound

This paper cites Resvit: A framework for deepfake videos detection.International Journal of Electrical and Com- puter Engineering Systems, 13(9):807–813, 2022.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Resvit: A framework for deepfake videos detection.International Journal of Electrical and Com- puter Engineering Systems, 13(9):807–813, 2022

Reference 3

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Observation 60ba1fcc-da68-48e5-900e-9fb314f0611b · outbound

This paper cites Capst: Leveraging capsule networks and temporal attention for accurate model attribution in deep-fake videos.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Capst: Leveraging capsule networks and temporal attention for accurate model attribution in deep-fake videos

Reference 4

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Observation e034ab4c-b763-46a4-a0a1-1ceac0b93764 · outbound

This paper cites Vivit: A video vision transformer.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Vivit: A video vision transformer

Reference 5

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Observation e84702e2-d060-4e4d-90d1-1fcd1f4ac9eb · outbound

This paper cites Reverse engineering of generative models: Inferring model hyperparameters from generated images.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Reverse engineering of generative models: Inferring model hyperparameters from generated images

Reference 6

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Observation f4f3fd68-c24c-4230-acae-7c538ebfa988 · outbound

This paper cites Openface 2.0: Facial behavior analysis toolkit.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Openface 2.0: Facial behavior analysis toolkit

Reference 7

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Observation fc498362-c139-416c-8b84-e42836274f28 · outbound

This paper cites Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), pages 813–824.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), pages 813–824

Reference 8

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Observation 9b6aa3b6-7d15-494d-bdac-2fbe6da070f3 · outbound

This paper cites Video face manipulation detection through ensemble of cnns.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Video face manipulation detection through ensemble of cnns

Reference 9

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Observation ba41a17f-518e-4dd1-a9c4-19b13ab26084 · outbound

This paper cites How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks).

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks)

Reference 10

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Source-reported events for the cited work

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Observation 36f3b3d7-b1eb-42ea-ab60-2454dcf6895a · outbound

This paper cites Neural head reen- actment with latent pose descriptors.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Neural head reen- actment with latent pose descriptors

Reference 11

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Observation 629fe52d-79d6-4182-b51b-0b6eb1696ccf · outbound

This paper cites Deep fakes: A looming challenge for privacy, democracy, and national security.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deep fakes: A looming challenge for privacy, democracy, and national security

Reference 12

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Observation f0fa9557-c6b7-40f7-beaf-4389ed66d175 · outbound

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

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Xception: Deep learning with depthwise separable convolutions

Reference 13

Resolution
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Observation ff8c0ef9-d687-4e68-8e38-ac350d343745 · outbound

This paper cites Deepfake detection using spatiotemporal convolutional networks.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deepfake detection using spatiotemporal convolutional networks

Reference 14

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Observation c5c6aff0-3091-4d47-a29a-badbc944079e · outbound

This paper cites an unresolved cited work.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Unresolved cited work

Reference 15

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Observation 970968e8-9620-45f5-833a-6f9af0cd5483 · outbound

This paper cites The deepfake detection challenge (dfdc) dataset.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes The deepfake detection challenge (dfdc) dataset

Reference 16

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Observation 75e205af-b0ff-4684-b71b-5fda61e7b098 · outbound

This paper cites The deepfake detection challenge (dfdc) preview dataset.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes The deepfake detection challenge (dfdc) preview dataset

Reference 17

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Observation 10a3d887-f7c6-4408-bfa4-e34224b45d61 · outbound

This paper cites Unmasking deep- fakes with simple features.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Unmasking deep- fakes with simple features

Reference 18

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Observation 8cd7a76a-d7e7-4253-87c0-22fe765dbd72 · outbound

This paper cites High-fidelity face manipulation with extreme poses and expressions.IEEE Transactions on Information Forensics and Security, 16:2218–2231, 2021.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes High-fidelity face manipulation with extreme poses and expressions.IEEE Transactions on Information Forensics and Security, 16:2218–2231, 2021

Reference 19

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Observation d16e7e9e-47f6-43e5-b6ac-3710202d6ac3 · outbound

This paper cites Towards discovery and attribution of open-world gan generated images.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Towards discovery and attribution of open-world gan generated images

Reference 20

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Observation 0ffbf88e-6abc-462e-81af-1fe6253d7484 · outbound

This paper cites Accessed: Dec.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Accessed: Dec

Reference 21

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Observation f8b2b205-799b-4b4b-a602-642fa3b726ec · outbound

This paper cites Accessed: Dec.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Accessed: Dec

Reference 22

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Observation 0d806d4b-02ac-44a2-98e4-36ac0c2af5dd · outbound

This paper cites Spatiotemporal inconsistency learning for deepfake video detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Spatiotemporal inconsistency learning for deepfake video detection

Reference 23

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Observation a09aa77e-55d4-4964-a7d2-e91aa9074f2e · outbound

This paper cites Deepfake detection by analyzing convolutional traces.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deepfake detection by analyzing convolutional traces

Reference 24

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Observation f74d0de6-6f3b-49aa-81cb-30c2bc29ee4b · outbound

This paper cites Multimodal forgery detection using ensemble learning.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Multimodal forgery detection using ensemble learning

Reference 25

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Observation c1c2b7f5-82e0-4131-ba8a-8495b157a385 · outbound

This paper cites Deep residual learning for image recognition.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deep residual learning for image recognition

Reference 26

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Observation ac78bdfd-8fdd-4715-be90-eaf9e69627fb · outbound

This paper cites Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, To- bias Weyand, Marco Andreetto, and Hartwig Adam.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, To- bias Weyand, Marco Andreetto, and Hartwig Adam

Reference 27

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Observation a766288d-f77e-41d2-8620-baa06906c8c5 · outbound

This paper cites Model attribution of face-swap deepfake videos.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Model attribution of face-swap deepfake videos

Reference 28

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Observation 479cb5e4-a936-46cb-9fc7-d6f19122aa75 · outbound

This paper cites Deeperforensics- 1.0: A large-scale dataset for real-world face forgery detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deeperforensics- 1.0: A large-scale dataset for real-world face forgery detection

Reference 29

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Observation aa382e75-7d77-4ff9-b25a-5d4ae8875105 · outbound

This paper cites Analyzing and improving the image quality of stylegan.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Analyzing and improving the image quality of stylegan

Reference 30

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Observation d84ace45-c65f-44e9-8b0f-23a77a42b21c · outbound

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FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Unresolved cited work

Reference 31

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Observation 064f6efc-00e0-43fa-8c46-53859ccaf87c · outbound

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FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Unresolved cited work

Reference 32

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Observation f1f666da-9f63-4cb7-b85e-5184aec63f73 · outbound

This paper cites Face x-ray for more general face forgery detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Face x-ray for more general face forgery detection

Reference 33

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9479a0b4-d042-4bf1-b7b7-08add9161f0f · outbound

This paper cites Exposing deepfake videos by detecting face warping artifacts.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Exposing deepfake videos by detecting face warping artifacts

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9c482b7e-6310-46e4-9cd3-fb93f67fb462 · outbound

This paper cites Exposing deepfake videos by detecting face warping artifacts.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Exposing deepfake videos by detecting face warping artifacts

Reference 35

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3e31498c-e55a-4c6e-88e3-0433a2e68dbf · outbound

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

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Celeb-df: A large-scale challenging dataset for deepfake forensics

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8c31ca27-5589-4363-acd8-3317f85aced9 · outbound

This paper cites Video swin transformer.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Video swin transformer

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 7c4cc786-3f01-40aa-80e8-2a845ad6f3f4 · outbound

This paper cites Deepfake detection: Current challenges and next steps.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deepfake detection: Current challenges and next steps

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b6b71770-5ab8-4634-8644-ee4e351d711e · outbound

This paper cites Do gans leave artificial fingerprints? In 2019 IEEE Conference on Multimedia Information Pro- cessing and Retrieval (MIPR), pages 506–511.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Do gans leave artificial fingerprints? In 2019 IEEE Conference on Multimedia Information Pro- cessing and Retrieval (MIPR), pages 506–511

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ff242731-e9dc-48f6-9c3d-a2a4c6c31ab1 · outbound

This paper cites Two-branch recurrent network for isolating deepfakes in videos.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Two-branch recurrent network for isolating deepfakes in videos

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 897d01c5-f731-4232-ad52-cf7df96bb57a · outbound

This paper cites Frame attention networks for facial expression recognition in videos.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Frame attention networks for facial expression recognition in videos

Reference 41

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c1ecbfd2-987e-44a9-a606-c7b32e338cb9 · outbound

This paper cites Emotions don’t lie: An audio-visual deepfake detection method using affective cues.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Emotions don’t lie: An audio-visual deepfake detection method using affective cues

Reference 42

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 32e4a1f7-f2ef-4fcb-92cb-fe83cf9e5f53 · outbound

This paper cites Nguyen, Junichi Yamagishi, and Isao Echizen.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Nguyen, Junichi Yamagishi, and Isao Echizen

Reference 43

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3c5585f5-de76-4ba1-8ec4-88bf2c99b085 · outbound

This paper cites Use of attentional warping for low- resolution deepfake detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Use of attentional warping for low- resolution deepfake detection

Reference 44

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6bad03fa-e53f-4fd9-8d05-367edffe4cad · outbound

This paper cites Deepfacelab: Integrated, flexible and extensible face-swapping framework.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Deepfacelab: Integrated, flexible and extensible face-swapping framework

Reference 45

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 63036286-8f67-4676-bd85-b500945d0b70 · outbound

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

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Faceforensics++: Learning to detect manipulated facial images

Reference 46

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 129f28a4-c111-4b94-9321-99093ae2c9a4 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 47

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3380e926-4f96-48fa-8917-466ed33516dc · outbound

This paper cites Parei- dolia face reenactment.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Parei- dolia face reenactment

Reference 48

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 82724447-51cb-4c22-bd3a-6c92e08b7111 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3d17632e-4c62-4c8f-974e-5cf6574dd9a3 · outbound

This paper cites Media forensics and deepfakes: an overview.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Media forensics and deepfakes: an overview

Reference 50

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1a87f733-511a-403f-b676-454328415c6e · outbound

This paper cites Forgerynir: Deep face forgery and detection in near-infrared scenario.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Forgerynir: Deep face forgery and detection in near-infrared scenario

Reference 51

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4e8911c4-fae4-46b9-9ff7-2d05eddedf27 · outbound

This paper cites Cbam: Convolu- tional block attention module.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Cbam: Convolu- tional block attention module

Reference 52

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 2ce9886e-50e5-4601-b02c-c415ddaef795 · outbound

This paper cites Facecontroller: Controllable attribute editing for face in the wild.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Facecontroller: Controllable attribute editing for face in the wild

Reference 53

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation edf8227c-fede-471d-ad8f-e26722a0af02 · outbound

This paper cites an unresolved cited work.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Unresolved cited work

Reference 54

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e2ad6248-7817-4c1f-bd8f-77a715088047 · outbound

This paper cites Multi-attentional deepfake detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Multi-attentional deepfake detection

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:15.508702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f4d5734b-3870-48c5-9afb-4513b42a9c34 · outbound

This paper cites One shot face swapping on megapixels.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes One shot face swapping on megapixels

Reference 56

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0e96fd61-1a08-4929-abe7-cdb73d42edaf · outbound

This paper cites Wilddeep- fake: A challenging real-world dataset for deepfake detection.

FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes Wilddeep- fake: A challenging real-world dataset for deepfake detection

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:15.198692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

Observation 60eaaa06-7c3f-4610-b965-0b90052bbc55 · inbound

Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks cites this paper.

Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

Reference 9

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arxiv_id, observed 2026-05-20T13:13:18.678916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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