Paper Citation Record · LEDGER

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation

As of 19 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.12339.

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

pith.paper-citation-record.v1
2505.12339 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:40:16.836583Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c0a85e1-720b-4562-aa00-d266bb63c8a6 · outbound

This paper cites In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

Reference 1

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Observation eb92f97e-2ea3-4817-b11c-da67b16be36f · outbound

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

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Mesonet: a compact facial video forgery detection network

Reference 2

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Observation 62c6edc9-a5e4-462b-9369-e668d3e43fc3 · outbound

This paper cites Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection

Reference 3

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Observation f7153ec1-f2de-4fc3-99e7-70d7b99f4123 · outbound

This paper cites Improving the efficiency and robustness of deepfakes detection through precise geometric features.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Improving the efficiency and robustness of deepfakes detection through precise geometric features

Reference 4

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Observation 68d03c31-12ba-4920-9dd7-1cbf3c07badd · outbound

This paper cites Forgerynet: A versatile benchmark for comprehensive forgery analysis.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Forgerynet: A versatile benchmark for comprehensive forgery analysis

Reference 5

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Observation a6712b7a-c4e2-4a1f-8f51-553ac1253eef · outbound

This paper cites Altfreezing for more general video face forgery detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Altfreezing for more general video face forgery detection

Reference 6

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Observation ed24ad03-bb54-42fe-841b-bbabc7d24b4d · outbound

This paper cites Tall: Thumbnail layout for deepfake video detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Tall: Thumbnail layout for deepfake video detection

Reference 7

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Observation 4ba77f00-7530-4a8f-8227-8c4786bab66d · outbound

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

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Faceforensics++: Learning to detect manipulated facial images

Reference 8

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Observation ef6e5122-8576-499a-8640-11e0a7c240cd · outbound

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

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Face x-ray for more general face forgery detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:40:17.425343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62bcab23-b2a2-4ccf-89a9-5ef43f9d0053 · outbound

This paper cites Denoising diffusion probabilistic models.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Denoising diffusion probabilistic models

Reference 10

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Observation 3a5d7f47-19fb-49ac-a1e5-52a16bc00991 · outbound

This paper cites Exposing DeepFake Videos By Detecting Face Warping Artifacts.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Exposing DeepFake Videos By Detecting Face Warping Artifacts

Reference 11

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Observation 16565a9e-c6b9-43a8-9bf2-964037ff37a6 · outbound

This paper cites Learning self-consistency for deepfake detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Learning self-consistency for deepfake detection

Reference 12

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Observation 5896a708-67f6-4fef-b4b0-0ab4ab6a5cb6 · outbound

This paper cites Detecting deepfakes with self-blended images.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Detecting deepfakes with self-blended images

Reference 13

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Source-reported events for the cited work

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Observation 88b070dd-67b1-4561-bda1-9744701ca213 · outbound

This paper cites Self- supervised learning of adversarial example: Towards good generalizations for deepfake detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Self- supervised learning of adversarial example: Towards good generalizations for deepfake detection

Reference 14

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Observation c151c035-e478-422f-a3b6-a0bc3cbfde09 · outbound

This paper cites Lisiam: Localization invariance siamese network for deepfake detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Lisiam: Localization invariance siamese network for deepfake detection

Reference 15

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Source-reported events for the cited work

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Observation f0aac4d3-5359-4dfd-8928-21198a0943db · outbound

This paper cites Dire for diffusion-generated image detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Dire for diffusion-generated image detection

Reference 16

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Source-reported events for the cited work

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Observation f569a950-a862-4a46-ade1-182d14d90f35 · outbound

This paper cites ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection

Reference 17

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Observation b98a562e-39d1-4fff-b3d8-a18f7a44cb8f · outbound

This paper cites Generalized Zero and Few-Shot Transfer for Facial Forgery Detection.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Generalized Zero and Few-Shot Transfer for Facial Forgery Detection

Reference 18

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Source-reported events for the cited work

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Observation bf2d596b-73fe-4773-ba64-0676529b5293 · outbound

This paper cites Few-shot forgery detection via guided adversarial interpolation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Few-shot forgery detection via guided adversarial interpolation

Reference 19

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Observation 541564dd-5c97-457f-88e5-a7594af5aa25 · outbound

This paper cites Uni- fied deep supervised domain adaptation and generalization.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Uni- fied deep supervised domain adaptation and generalization

Reference 20

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Observation 8bd2ac04-c6b4-4b9d-a3db-25b79e9a1c56 · outbound

This paper cites Identity-driven multimedia forgery detection via reference assistance.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Identity-driven multimedia forgery detection via reference assistance

Reference 21

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5bda92f-084c-4a2c-82ea-4dc5a94ddbd4 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Temporal Ensembling for Semi-Supervised Learning

Reference 22

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Observation 92e567ed-6be1-4485-a3a6-572fcc76c581 · outbound

This paper cites Open-World Semi-Supervised Learning.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Open-World Semi-Supervised Learning

Reference 23

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Observation a807dce7-3e7a-42a6-b291-8b46da66e676 · outbound

This paper cites Virtual adver- sarial training: a regularization method for supervised and semi-supervised learn- ing.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Virtual adver- sarial training: a regularization method for supervised and semi-supervised learn- ing

Reference 24

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d24c116-dd47-4422-af92-47dd2dd53c3e · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 25

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Observation e4601ee1-e7fe-4e3b-85af-07fec8b9c0f2 · outbound

This paper cites Unsupervised domain adaptation for semantic segmentation via class-balanced self-training.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Unsupervised domain adaptation for semantic segmentation via class-balanced self-training

Reference 26

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Observation 11079582-f971-46c5-9503-48cd8c31d90e · outbound

This paper cites Adversarial discrim- inative domain adaptation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Adversarial discrim- inative domain adaptation

Reference 27

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Observation a77232cf-9169-4845-971f-7a128b30379c · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Unsupervised domain adaptation by backpropagation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:40:17.159627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d98ab73-d478-49b0-8919-756f5ba77fea · outbound

This paper cites Separate to adapt: Open set domain adaptation via progressive separation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Separate to adapt: Open set domain adaptation via progressive separation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:40:17.138451Z

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Observation e17d5c64-4d3a-4db9-8408-eacb69fec2db · outbound

This paper cites Domainforensics: Exposing face forgery across domains via bi-directional adaptation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Domainforensics: Exposing face forgery across domains via bi-directional adaptation

Reference 30

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Observation 97d6ade3-07fe-4611-87b8-d6e5066dfb29 · outbound

This paper cites Fine-grained open-set deepfake detection via unsupervised domain adaptation.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Fine-grained open-set deepfake detection via unsupervised domain adaptation

Reference 31

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Observation 9ff6dbd8-28c9-4d51-867e-b991cc77a065 · outbound

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

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Celeb-df: A large- scale challenging dataset for deepfake forensics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:40:17.074461Z

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Observation 655cae27-0072-4af7-aa72-76cf6aa0e717 · outbound

This paper cites The DeepFake Detection Challenge (DFDC) Dataset.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation The DeepFake Detection Challenge (DFDC) Dataset

Reference 33

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Observation d4f68605-5e17-4b45-a9fb-185d2a29e9a0 · outbound

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

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Xception: Deep learning with depthwise separable convolutions

Reference 34

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Observation 9a85d8c6-a9ab-4f9c-8873-3ebb46cd9a4d · outbound

This paper cites Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:40:17.041975Z

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Observation d7921354-779d-4466-a009-f1e18c28963f · outbound

This paper cites Deep residual learning for image recognition.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Deep residual learning for image recognition

Reference 36

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Observation aa6a71f8-9017-4e56-af1f-fe2b141f7670 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolu- tional neural networks.

Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation Efficientnet: Rethinking model scaling for convolu- tional neural networks

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:40:16.836583Z

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Pith citing papers

No inbound Pith citation observations are available.