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Paper Citation Record · LEDGER

Enhancing Deepfake Detection using SE Block Attention with CNN

As of 18 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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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 2026-08-18T06:34:40.430872+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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verified fuzzy
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 2026-08-18T06:34:40.430872+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:e4a40adec16bf5b95d23b34cd78b09640c4b91b2165ec2dbc5bb22eda53ea30b

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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verified fuzzy
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 2026-08-18T06:34:40.430872+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:be11797dc36314bc2b5bf64efb9f256cb02a1d1553fa2de9509c27033cd1d608

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T04:22:56.861360Z digest=sha256:65547a747513764d9a05aa2bdca2393bd3fa079c329acad49b3de78bf062ef81

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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verified fuzzy
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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T04:22:56.865959Z digest=sha256:261e21eaaf54636e921c2197b0c168cde1214fdea20b03e257da47727df75895

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T04:22:56.880850Z digest=sha256:9b4af308189ed1615c636748132cbd3a1fe49d9c53ab8e2103c41199fc0500c0

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=sha256:26293b78b7fd5b135a83ce1ba9581415a1c7fee6079be8e884a1befecc75b802

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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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 2026-08-18T06:34:40.430872+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.

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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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T04:22:56.904612Z digest=sha256:d8cc459dfbd039a4e7aac6b642a06fc9671f36f1b41c3c28ae5e6b99f80798c5

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T04:22:56.908858Z digest=sha256:a2600ede0292ae23c9fa8810d0b92065b0b41246ad2710e24f0fcd23a0bd744e

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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raw_fallback, observed 2026-08-07T04:22:57.045776Z

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.

source=pdf_text observed=2026-08-07T04:22:56.913311Z digest=sha256:f17aa46ea04c97dbfe097cb4ad929c35538cb2d9881ef129b100251b57c96e85

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 2026-08-18T06:34:40.430872+00:00.

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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 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

No inbound Pith citation observations are available.