Paper Citation Record · LEDGER

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

As of 21 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 3 inbound Pith citation observations for arXiv:2411.08148.

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

pith.paper-citation-record.v1
2411.08148 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T22:00:00.231671Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:04:40.163230Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:13:18.722536Z

Reference resolution

56 of 56 outbound references displayed

verified exact1
verified fuzzy54
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parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff2fab4d-1f2f-4a1b-b72b-3d05f3b5c184 · outbound

This paper cites Deep fake video detection using transfer learning approach,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deep fake video detection using transfer learning approach,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.777982Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f36d4e8-3789-4830-a236-09294629380c · outbound

This paper cites Fretal: Generalizing deep- fake detection using knowledge distillation and representation learning,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Fretal: Generalizing deep- fake detection using knowledge distillation and representation learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.755815Z

Source-reported events for the cited work

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Observation f45453e5-5b38-49b2-afcd-96764d7d55c3 · outbound

This paper cites Domain generalization for face forgery detection by style transfer,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Domain generalization for face forgery detection by style transfer,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.707444Z

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Observation 685d14ed-45af-4311-8020-2259f02786e4 · outbound

This paper cites Robustness and generalizability of deepfake detection: A study with diffusion models,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Robustness and generalizability of deepfake detection: A study with diffusion models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.682655Z

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Observation ae8723af-8cfd-42b0-ae1a-acd21108dd66 · outbound

This paper cites How generalizable are deepfake image detectors? an empirical study,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization How generalizable are deepfake image detectors? an empirical study,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.661272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 697b1668-58ee-4a16-b140-756fdf8d30fb · outbound

This paper cites A review of deep learning-based approaches for deepfake content detection,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization A review of deep learning-based approaches for deepfake content detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.637908Z

Source-reported events for the cited work

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Observation 8b37027c-6472-4ab8-bc3f-1743c546a2d0 · outbound

This paper cites Revisiting generalizability in deepfake detection: Improving metrics and stabilizing transfer,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Revisiting generalizability in deepfake detection: Improving metrics and stabilizing transfer,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.614919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e4f5df8-8534-41df-ba8f-d4d53c641abb · outbound

This paper cites On the vulnerability of deepfake detectors to attacks generated by denoising diffusion models,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization On the vulnerability of deepfake detectors to attacks generated by denoising diffusion models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.583611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f9fdc26-b99c-427e-8d13-984e2560337e · outbound

This paper cites Evading deepfake-image detectors with white- and black-box attacks,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Evading deepfake-image detectors with white- and black-box attacks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.558702Z

Source-reported events for the cited work

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Observation aa108124-65d0-4407-bb6e-46566b70d243 · outbound

This paper cites Evad- ing deepfake detectors via adversarial statistical consistency,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Evad- ing deepfake detectors via adversarial statistical consistency,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.535938Z

Source-reported events for the cited work

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Observation c08a38b7-3770-4c3d-b8db-4580d6c27cbe · outbound

This paper cites Adversarially robust deepfake video detection,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Adversarially robust deepfake video detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.514537Z

Source-reported events for the cited work

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Observation 0220feb3-47fa-4e2a-b401-2866484845b5 · outbound

This paper cites D4: Detection of adversarial diffusion deepfakes using disjoint ensembles,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization D4: Detection of adversarial diffusion deepfakes using disjoint ensembles,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.493345Z

Source-reported events for the cited work

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Observation 8dafdae3-e62b-4d95-aac9-71fe4cefe598 · outbound

This paper cites Adversari- ally robust deepfake detection via adversarial feature similarity learning,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Adversari- ally robust deepfake detection via adversarial feature similarity learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.464460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db73785e-c7f2-438e-9ca3-2ccd16a881b9 · outbound

This paper cites Adver- sarial threats to deepfake detection: A practical perspective,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Adver- sarial threats to deepfake detection: A practical perspective,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.446757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 118bb977-e7b5-4c2c-96c3-b586cc01fe64 · outbound

This paper cites 2d- malafide: Adversarial attacks against face deepfake detection systems,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization 2d- malafide: Adversarial attacks against face deepfake detection systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.423318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70f4a039-b2e2-4e42-9408-ddf7828cdba8 · outbound

This paper cites Metamorphic testing-based adversarial attack to fool deepfake detectors,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Metamorphic testing-based adversarial attack to fool deepfake detectors,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.396015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c65284a9-cb08-48bc-bef9-0eaf4d2344f3 · outbound

This paper cites Evading deepfake detectors via high quality face pre-processing methods,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Evading deepfake detectors via high quality face pre-processing methods,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.366400Z

Source-reported events for the cited work

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Observation e57f6877-bedf-4d67-a30c-58997ea22a7d · outbound

This paper cites Making deepfakes more spurious: Evading deep face forgery detection via trace removal attack,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Making deepfakes more spurious: Evading deep face forgery detection via trace removal attack,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.337812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c4676750-8da2-400d-8762-113e38a32ce6 · outbound

This paper cites Continuous fake media detection: Adapting deepfake detectors to new generative tech- niques,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Continuous fake media detection: Adapting deepfake detectors to new generative tech- niques,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.317834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 76e135ef-ce84-4f18-88e2-1c20de045ccf · outbound

This paper cites On first-order meta- learning algorithms,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization On first-order meta- learning algorithms,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.284607Z

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Observation 08906d73-07eb-483b-a229-9d48c1f88fad · outbound

This paper cites Deep learning for deepfakes creation and de- tection: A survey,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deep learning for deepfakes creation and de- tection: A survey,

Reference 21

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Source-reported events for the cited work

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Observation c0940017-fac2-44d5-8399-6b0a1c091fa7 · outbound

This paper cites The creation and detection of deep- fakes: A survey,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization The creation and detection of deep- fakes: A survey,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.207878Z

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Observation 0ba807f5-762c-44b8-9a8c-4b4c5ee91d8e · outbound

This paper cites Deepfakes generation and detection: A short survey,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deepfakes generation and detection: A short survey,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.187241Z

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Observation ca4de35c-be58-47a5-9f52-3fe91cff2d53 · outbound

This paper cites Are face detection models biased?,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Are face detection models biased?,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.165741Z

Source-reported events for the cited work

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Observation a69f1e7a-991d-452d-99c5-be096f3387f4 · outbound

This paper cites Df-platter: Multi-face heterogeneous deepfake dataset,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Df-platter: Multi-face heterogeneous deepfake dataset,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.142153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f6fd845-a060-438e-859f-2ece8d874ca6 · outbound

This paper cites Deephy: On deepfake phylogeny,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deephy: On deepfake phylogeny,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.110486Z

Source-reported events for the cited work

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Observation a94e3020-e881-4bf8-b8b4-6aee1779745b · outbound

This paper cites Detecting and grounding multi-modal media manipulation and beyond,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Detecting and grounding multi-modal media manipulation and beyond,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.087468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f8be888-6faa-4183-a9c0-3c671524bdb8 · outbound

This paper cites Deter: Detecting edited regions for deterring generative manipulations,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deter: Detecting edited regions for deterring generative manipulations,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.062222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61978913-4ec7-4f08-bda3-36529dfb6d31 · outbound

This paper cites Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-12T22:00:00.329335Z

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Observation 20b87c9b-7b0f-41d3-97bd-c51d1b86bba7 · outbound

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

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Model attribution of face-swap deepfake videos,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:01.026682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2558a8f0-191c-43d8-a8a6-81bfc6db4a22 · outbound

This paper cites Au- tosplice: A text-prompt manipulated image dataset for media forensics,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Au- tosplice: A text-prompt manipulated image dataset for media forensics,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.989634Z

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Observation 23d46f2c-13fb-4af7-b49c-8c50925af816 · outbound

This paper cites DeepFakes: a New Threat to Face Recognition? Assessment and Detection.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization DeepFakes: a New Threat to Face Recognition? Assessment and Detection

Reference 32

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Observation 3c6212ec-db2a-4e88-ae00-d9b2085a37fc · outbound

This paper cites On the detection of digital face manipulation,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization On the detection of digital face manipulation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.970999Z

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Observation 931c8cc8-f569-4b94-ae9b-de31b7e18d90 · outbound

This paper cites Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detec- tion,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Ganprintr: Improved fakes and evaluation of the state of the art in face manipulation detec- tion,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.952589Z

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Observation dc32ff90-a6ab-48cb-9a83-076f28f46179 · outbound

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

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Celeb-df: A large-scale challenging dataset for deepfake forensics,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.921762Z

Source-reported events for the cited work

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Observation 853ec17e-92e1-4e6c-ba5c-8f31860853b5 · outbound

This paper cites Faceforensics++: Learning to detect ma- nipulated facial images,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Faceforensics++: Learning to detect ma- nipulated facial images,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.897137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 992b4fc7-3b91-4ddc-b2d8-3a3c6cf7affd · outbound

This paper cites Face forensics in the wild,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Face forensics in the wild,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.862633Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 612f3cdd-3e78-4914-a7d7-63f723a931e0 · outbound

This paper cites Dfgc 2021: A deepfake game competition,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Dfgc 2021: A deepfake game competition,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.834223Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b998959-85e2-4711-aaa4-8bb895ea87e1 · outbound

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

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Wilddeep- fake: A challenging real-world dataset for deepfake detection,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.807159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42df6558-1058-456e-a921-96a9a3a4a41f · outbound

This paper cites Exposing deep fakes using inconsistent head poses,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Exposing deep fakes using inconsistent head poses,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.786565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2f155c8f-622e-481d-8ae1-7b3d0e5deb2f · outbound

This paper cites Deepfake generation, detection and datasets: a rapid-review,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deepfake generation, detection and datasets: a rapid-review,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.766035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0d96566-0d57-48fc-920c-74c5198a45fb · outbound

This paper cites Ai vs. human vision: A comparative analysis for distinguishing ai-generated and natural images,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Ai vs. human vision: A comparative analysis for distinguishing ai-generated and natural images,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.747064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ebd1574-9f49-492b-ab40-36f6b94ce4d2 · outbound

This paper cites Bobulski and M.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Bobulski and M

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.723447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 622bc0b5-79a2-43bf-8851-dc251b5bbf9b · outbound

This paper cites Deepfake on face and expression swap: A review,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deepfake on face and expression swap: A review,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.689946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8effeee7-0b1e-48bd-afb5-3a49d5def7a1 · outbound

This paper cites Deepfake detection for human face images and videos: A survey,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deepfake detection for human face images and videos: A survey,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.671097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8000a233-fa28-41dc-a1af-3cd96c293936 · outbound

This paper cites A systematic review on fake image creation techniques,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization A systematic review on fake image creation techniques,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.653709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 27cd36c7-099a-41ef-bca2-84abf8634625 · outbound

This paper cites Open-set deepfake detection to fight the unknown,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Open-set deepfake detection to fight the unknown,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.609151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0c70634b-3486-421b-9915-6e7845f23e76 · outbound

This paper cites Advshadow: Evading deepfake detection via adversarial shadow attack,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Advshadow: Evading deepfake detection via adversarial shadow attack,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.576986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f999825-74d4-4a51-884e-61b1e34fad2d · outbound

This paper cites Evading deepfake-image detectors with white- and black-box attacks,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Evading deepfake-image detectors with white- and black-box attacks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.551386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37883dd2-19bc-4769-a144-926d16ea762d · outbound

This paper cites An analysis of recent advances in deepfake image detection in an evolving threat landscape,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization An analysis of recent advances in deepfake image detection in an evolving threat landscape,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.530045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10422c48-4b58-43f4-b4ee-5fb284f4ca15 · outbound

This paper cites Deepfake attacks: Generation, detection, datasets, challenges, and research directions,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deepfake attacks: Generation, detection, datasets, challenges, and research directions,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.507091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8103948-1328-4c4d-8778-3a5b6d431344 · outbound

This paper cites Detecting and grounding multi- modal media manipulation,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Detecting and grounding multi- modal media manipulation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.466412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c00678bb-373d-4f9c-8ed7-905a5b4dcb6f · outbound

This paper cites GANprintR: Improved Fakes and Evaluation of the State-of-the-Art in Face Manipulation Detection,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization GANprintR: Improved Fakes and Evaluation of the State-of-the-Art in Face Manipulation Detection,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.424826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 484d4a58-ebea-4286-9ec6-598476bb33fe · outbound

This paper cites Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.396873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b320e6fd-d3e6-4dee-87e2-4cfae12b735a · outbound

This paper cites Deep- fakebench: A comprehensive benchmark of deepfake detec- tion,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Deep- fakebench: A comprehensive benchmark of deepfake detec- tion,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.374208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28e367e3-604f-48e3-9793-689e378608f4 · outbound

This paper cites Openforensics: Large-scale challenging dataset for multi-face forgery detection and segmentation in-the-wild,.

Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization Openforensics: Large-scale challenging dataset for multi-face forgery detection and segmentation in-the-wild,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:00:00.351550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 0a489483-fe03-4cf8-892e-20d09bf6a2ae · inbound

Graph-based Fake Account Detection: A Survey cites this paper.

Graph-based Fake Account Detection: A Survey Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T19:04:40.163230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a007d427-12cb-4213-aa26-8102cf1609f8 · inbound

Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems cites this paper.

Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T14:34:10.793451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fb410c7-befc-48f6-b97c-76cd799dc54c · 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 Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:13:18.726004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.