Paper Citation Record · LEDGER

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2508.16284.

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

pith.paper-citation-record.v1
2508.16284 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:25:42.470690Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T20:02:56.057102Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:55.100668Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy15
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ba63fda-efb9-43c4-9d4a-ad0ab993a223 · outbound

This paper cites write newline.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T17:25:42.419455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8535ebe8-d83b-4be2-b2bd-08af7f837ee2 · outbound

This paper cites Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T17:25:42.423410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d54206f-8ce2-439c-a9f9-11f8269f58dc · outbound

This paper cites Noiseprint: A cnn-based camera model fingerprint.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Noiseprint: A cnn-based camera model fingerprint

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.654334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a0746406-dad2-4391-97da-46248c896410 · outbound

This paper cites Edgeface: Efficient face recognition model for edge devices.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Edgeface: Efficient face recognition model for edge devices

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.645575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b72e4ef6-6afe-4ece-a908-6a678ed6e0fb · outbound

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

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Trufor: Leveraging all-round clues for trustworthy image forgery detection and localization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.636561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 414ddefe-4089-499e-b411-4254433dd877 · outbound

This paper cites Fantasyid: A dataset for detecting digital manipulations of id-documents.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Fantasyid: A dataset for detecting digital manipulations of id-documents

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.628005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1e55799e-23dc-40e2-b594-b66b6eb5131f · outbound

This paper cites Deepid challenge of detecting synthetic manipulations in id documents.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Deepid challenge of detecting synthetic manipulations in id documents

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.619748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 73a195bb-0e06-47e2-af47-fd7d1a920aad · outbound

This paper cites Forgery-aware adaptive transformer for generalizable synthetic image detection.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Forgery-aware adaptive transformer for generalizable synthetic image detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.610790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a87389ac-63e0-4ba3-ad7d-b85d96642f4d · outbound

This paper cites Decoupled Weight Decay Regularization.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Decoupled Weight Decay Regularization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T17:25:42.443964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc101f1-084a-490a-9a57-9f55d9ff6671 · outbound

This paper cites Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.601696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31bf0349-52ff-4da7-aae3-ba97c127c2f8 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.592415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 89f39817-2e7a-4195-9854-b0901040a43f · outbound

This paper cites FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents FakeIDet: Exploring Patches for Privacy-Preserving Fake ID Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T17:25:42.451997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0a44ab0-7b31-4fd0-a9ec-074510b855dc · outbound

This paper cites Towards universal fake image detectors that generalize across generative models.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Towards universal fake image detectors that generalize across generative models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.583446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26d4faae-19ff-4543-9f17-61a28472a762 · outbound

This paper cites Few-shot learning: Expanding id cards presentation attack detection to unknown id countries.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Few-shot learning: Expanding id cards presentation attack detection to unknown id countries

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.574241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19c75791-4bfa-4f59-9f2e-b75284ed418c · outbound

This paper cites First competition on presentation attack detection on id card.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents First competition on presentation attack detection on id card

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.564368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6dd206d5-c341-4f58-a30e-21b32204ccd7 · outbound

This paper cites Exploring multi-modal fusion for image manipulation detection and localization.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Exploring multi-modal fusion for image manipulation detection and localization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.555536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24f979da-5264-4ddc-9a88-73079bceeae6 · outbound

This paper cites Forensics-bench: A comprehensive forgery detection benchmark suite for large vision language models.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Forensics-bench: A comprehensive forgery detection benchmark suite for large vision language models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.546160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39a9e6cd-2539-44ea-bf5d-6f80645da95d · outbound

This paper cites Research on identity document image tampering detection based on texture understanding and multistream networks.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Research on identity document image tampering detection based on texture understanding and multistream networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.536873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a89c14fe-9a17-4a83-bafb-b4f640d7b19c · outbound

This paper cites Deep learning-based forgery attack on document images.

EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents Deep learning-based forgery attack on document images

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:25:42.527397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation ed76f55e-2247-49b0-b034-948a69313ef6 · inbound

From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection cites this paper.

From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:55.102989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.