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Paper Citation Record · LEDGER

Enhancing Deepfake Detection using SE Block Attention with CNN

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.10683.

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

pith.paper-citation-record.v1
2506.10683 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:22:56.922045Z

measured 21 of 21 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

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy12
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f83f217-8c53-41d3-a7f5-1f9b102e0a3b · outbound

This paper cites The creation and detection of deepfakes: A survey,.

Enhancing Deepfake Detection using SE Block Attention with CNN The creation and detection of deepfakes: A survey,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-07T04:22:57.249198Z

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.

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Observation d43e07ff-cdb3-4790-9781-e7ece3a9deaa · outbound

This paper cites A survey on deepfake video detection,.

Enhancing Deepfake Detection using SE Block Attention with CNN A survey on deepfake video detection,

Reference 2

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raw_fallback, observed 2026-08-07T04:22:57.233565Z

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.

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Observation 288fbc0a-5021-460a-b7b4-22355fcb5ef0 · outbound

This paper cites A comprehensive overview of deepfake: Generation, detection, datasets, and opportuni- ties,.

Enhancing Deepfake Detection using SE Block Attention with CNN A comprehensive overview of deepfake: Generation, detection, datasets, and opportuni- ties,

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7fb9fb66-d61a-4fca-bcec-7f016fe07fa0 · outbound

This paper cites Digital and physical face attacks: Reviewing and one step further,.

Enhancing Deepfake Detection using SE Block Attention with CNN Digital and physical face attacks: Reviewing and one step further,

Reference 4

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raw_fallback, observed 2026-08-07T04:22:57.209258Z

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.

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Observation 6fe5d5fe-f19b-449a-872f-3cd575487a51 · outbound

This paper cites Implicit identity driven deepfake face swapping detection,.

Enhancing Deepfake Detection using SE Block Attention with CNN Implicit identity driven deepfake face swapping detection,

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:56.845775Z digest=sha256:1859469917b2ae555b4727111f0216f246859796958a447a6ee98d5a19b211dc

Observation 646a8c0c-0e25-4f8a-a2e6-68d996a846e6 · outbound

This paper cites Deepfake attacks: Generation, detection, datasets, challenges, and research direc- tions,.

Enhancing Deepfake Detection using SE Block Attention with CNN Deepfake attacks: Generation, detection, datasets, challenges, and research direc- tions,

Reference 6

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raw_fallback, observed 2026-08-07T04:22:57.184326Z

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.

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Observation 776b4952-d7cf-40e1-bff7-7b950cb10a14 · outbound

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

Enhancing Deepfake Detection using SE Block Attention with CNN Faceforensics++: Learning to detect manipulated facial images,

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:56.856671Z digest=sha256:674b70865d47d2358bee213cd8416984624a2c6477a4632c26de52d322b36bee

Observation 0cedbfad-c021-487e-95f5-d32452521183 · outbound

This paper cites On the de- tection of digital face manipulation,.

Enhancing Deepfake Detection using SE Block Attention with CNN On the de- tection of digital face manipulation,

Reference 8

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raw_fallback, observed 2026-08-07T04:22:57.159090Z

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.

source=pdf_text observed=2026-08-07T04:22:56.861360Z digest=sha256:173139ef2d3e0fe56e86b5cc7817e225cd026d186a8e7d4d1c3dbf1b8d3c8621

Observation a92f8fe9-95dc-451c-ab36-aad24080e69a · outbound

This paper cites An attention module for convolutional neural networks,.

Enhancing Deepfake Detection using SE Block Attention with CNN An attention module for convolutional neural networks,

Reference 9

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raw_fallback, observed 2026-08-07T04:22:57.144662Z

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.

source=pdf_text observed=2026-08-07T04:22:56.865959Z digest=sha256:95444df4dd628a01057fb308fe9467f9484ab74e81b29b0bfe28d51f37ac9bc1

Observation 1d102d3c-3ba1-4f52-872b-fd739e3f4398 · outbound

This paper cites Deepfake Video Detection Using Convolutional Vision Transformer.

Enhancing Deepfake Detection using SE Block Attention with CNN Deepfake Video Detection Using Convolutional Vision Transformer

Reference 10

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no resolver link, observed 2026-08-07T04:22:56.870431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 27385cd0-6068-4530-871c-467d9724efd1 · outbound

This paper cites Squeeze-and-excitation networks,.

Enhancing Deepfake Detection using SE Block Attention with CNN Squeeze-and-excitation networks,

Reference 11

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Unavailable: canonical work link unavailable.

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Observation 5e00de6d-8f29-4130-b6c2-d79ccc598791 · outbound

This paper cites Deepfake detection: A systematic literature review,.

Enhancing Deepfake Detection using SE Block Attention with CNN Deepfake detection: A systematic literature review,

Reference 12

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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.

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Observation f27c76ca-59eb-4e04-8b0d-c5d44b29e749 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Deepfake Detection using SE Block Attention with CNN An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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no resolver link, observed 2026-08-07T04:22:56.885444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:56.885444Z digest=sha256:3aadcc85793b4f00fd61b5ccebb399a967ea28e4be99b9c7fc1ae2115d64ee5b

Observation fe74ae1b-9f63-4340-b687-31b5c9ce77cc · outbound

This paper cites Combining efficientnet and vision transformers for video deepfake detection,.

Enhancing Deepfake Detection using SE Block Attention with CNN Combining efficientnet and vision transformers for video deepfake detection,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T04:22:57.104697Z

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.

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Observation bd53a2eb-5864-4a58-9514-316eafe9d8da · outbound

This paper cites Cvt: Introducing convolutions to vision transformers,.

Enhancing Deepfake Detection using SE Block Attention with CNN Cvt: Introducing convolutions to vision transformers,

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:56.894636Z digest=sha256:e907226c73b173096bd69566c63ebba8560c0e6c13f5f25ff503897d9db13ea5

Observation bab9aae9-ccc6-40d4-bd9f-1ea83285633e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Enhancing Deepfake Detection using SE Block Attention with CNN Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 16

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no resolver link, observed 2026-08-07T04:22:56.899766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4010b908-8140-44fc-a140-7895f71912f5 · outbound

This paper cites 3d cnn architectures and attention mechanisms for deepfake detection,.

Enhancing Deepfake Detection using SE Block Attention with CNN 3d cnn architectures and attention mechanisms for deepfake detection,

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-07T04:22:57.077544Z

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.

source=pdf_text observed=2026-08-07T04:22:56.904612Z digest=sha256:25fdfaefc3f2a7f3aa09df76bab3cfdffd024f07f38bef51e9edc3f6dcaa1841

Observation c0f13807-31d9-4aec-adc2-322153a2344a · outbound

This paper cites A style-based generator architecture for generative adversarial networks,.

Enhancing Deepfake Detection using SE Block Attention with CNN A style-based generator architecture for generative adversarial networks,

Reference 18

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raw_fallback, observed 2026-08-07T04:22:57.061451Z

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.

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Observation cfa539c9-c6b7-4c4a-b7f1-81add9876f3d · outbound

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

Enhancing Deepfake Detection using SE Block Attention with CNN Fake- buster: A lightweight solution for deepfake detection,

Reference 19

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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.

source=pdf_text observed=2026-08-07T04:22:56.913311Z digest=sha256:0146f3dd005e9093d20371b1a5083b691f710670ef0640eed99d58065e1a0c16

Observation 07b1722e-761b-4a23-ba31-b453a93c5c37 · outbound

This paper cites Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM.

Enhancing Deepfake Detection using SE Block Attention with CNN Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

Reference 20

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local_arxiv, observed 2026-08-07T04:22:56.964746Z

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.

source=pdf_text observed=2026-08-07T04:22:56.917467Z digest=sha256:a9a9dfbde7d5c1891eeed72c36ce19005e6361954e87a0bf60909f9ce3a17e33

Observation 8eec4be7-6fd2-4480-94e3-ac2f7590ac6d · outbound

This paper cites kaggle-dfdc: Deepfake Detection Challenge (DFDC) solu- tion,.

Enhancing Deepfake Detection using SE Block Attention with CNN kaggle-dfdc: Deepfake Detection Challenge (DFDC) solu- tion,

Reference 21

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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.

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Pith citing papers

No inbound Pith citation observations are available.