Paper Citation Record · LEDGER

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement

As of 19 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2509.07178.

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

pith.paper-citation-record.v1
2509.07178 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:48:36.006638Z

measured 27 of 27 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T00:29:28.216754Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy22
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8dd2f51d-5dc7-44c1-8ad9-422a96cf6ce1 · outbound

This paper cites Discrete cosine transform.IEEE transactions on Computers, 100:90–93, 2006.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Discrete cosine transform.IEEE transactions on Computers, 100:90–93, 2006

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:41.752862Z

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 04f40db5-cf58-49b4-8227-e8464dc3a661 · outbound

This paper cites Exposing the limits of deepfake detection using novel facial mole attack: A perceptual black-box adversar- ial attack study.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Exposing the limits of deepfake detection using novel facial mole attack: A perceptual black-box adversar- ial attack study

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:41.466549Z

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 42d1d4bc-b124-4e2f-9974-8832bf4bca80 · outbound

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

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Evading deepfake-image detectors with white-and black-box attacks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:41.174248Z

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 04cd408e-941b-4c57-8011-f5b9869eae2d · outbound

This paper cites Towards evaluating the robustness of neural net- works.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Towards evaluating the robustness of neural net- works

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T22:48:33.272917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d5e7d9a-3bc2-41e0-b517-a49abba13cfe · outbound

This paper cites Restricted black-box adversarial attack against deepfake face swapping.IEEE Transactions on Information F orensics and Security, 18:2596–2608, 2023.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Restricted black-box adversarial attack against deepfake face swapping.IEEE Transactions on Information F orensics and Security, 18:2596–2608, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:40.943486Z

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 b48a56c7-c444-41b2-9fbc-1f5c19e7b31d · outbound

This paper cites An adversarial attack approach for explainable ai evaluation on deepfake detection models.Computers & Security, 139: 103684, 2024.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement An adversarial attack approach for explainable ai evaluation on deepfake detection models.Computers & Security, 139: 103684, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:40.600419Z

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 22961e6d-c6fe-4aaa-8c2e-b4d9885c4d29 · outbound

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

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Evad- ing deepfake detectors via adversarial statistical consistency

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:40.364444Z

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 96b1c979-3bfb-45ca-8a8c-c7e868003e2e · outbound

This paper cites Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:40.115214Z

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 8636c9a3-4c22-4456-9081-bd26fc11a2b3 · outbound

This paper cites Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:39.881726Z

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 efcc473c-2e22-4b6a-9009-48eb6ce70c62 · outbound

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

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement On the vulnerability of deepfake detectors to attacks generated by denoising diffusion models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:39.631992Z

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 2f2a71aa-abef-4f99-81db-296af46e4603 · outbound

This paper cites Deepfake: a social construction of technology perspective.Current Issues in Tourism, 24(13):1798–1802, 2021.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Deepfake: a social construction of technology perspective.Current Issues in Tourism, 24(13):1798–1802, 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:39.359779Z

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 33167577-d326-4240-b9f4-2a8d081229e1 · outbound

This paper cites Frequency domain regular- ization for iterative adversarial attacks.Pattern Recognition, 134:109075, 2023.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Frequency domain regular- ization for iterative adversarial attacks.Pattern Recognition, 134:109075, 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:39.085583Z

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 93444d89-826d-402d-9c17-748b6eee65d0 · outbound

This paper cites Generalizing face forgery de- tection with high-frequency features.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Generalizing face forgery de- tection with high-frequency features

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:38.823646Z

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 72ff970f-75e9-4491-ab23-4f92df0820ce · outbound

This paper cites Ava: Incon- spicuous attribute variation-based adversarial attack bypassing deepfake detection.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Ava: Incon- spicuous attribute variation-based adversarial attack bypassing deepfake detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:38.608559Z

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 ab86159c-6ebd-45ac-a7ae-098e65c7826a · outbound

This paper cites Core: Consistent representation learning for face forgery detection.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Core: Consistent representation learning for face forgery detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:38.245987Z

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 3833e4ba-9490-4ba0-b5b7-2e0525a18385 · outbound

This paper cites Thinking in fre- quency: Face forgery detection by mining frequency-aware clues.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Thinking in fre- quency: Face forgery detection by mining frequency-aware clues

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:38.052654Z

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 b927f37c-ce96-4b03-97ea-39bc9f8f13db · outbound

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

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Faceforensics++: Learning to detect manipulated facial images

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T22:48:35.109407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93ac00d2-dc01-47b1-9f0c-06833dd731fa · outbound

This paper cites Deep person generation: A survey from the perspective of face, pose, and cloth synthesis.ACM Computing Surveys, 55(12):1–37, 2023.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Deep person generation: A survey from the perspective of face, pose, and cloth synthesis.ACM Computing Surveys, 55(12):1–37, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:37.761181Z

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 60e28520-bdec-473f-a440-5eeb5174d75f · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T22:48:35.264423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be7be327-b2b7-4817-b772-0f3f83ced474 · outbound

This paper cites Curve and surface smoothing without shrinkage.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Curve and surface smoothing without shrinkage

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:37.488963Z

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 ec57f6db-ce05-4ab2-91b5-1bafbd519d0b · outbound

This paper cites Bilateral filtering for gray and color images.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Bilateral filtering for gray and color images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:37.271720Z

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 8dc9d9ef-3e3b-4de0-932e-d79fc4685204 · outbound

This paper cites A robust open- set multi-instance learning for defending adversarial attacks in digital image.IEEE Transactions on Information F orensics and Security, 2023.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement A robust open- set multi-instance learning for defending adversarial attacks in digital image.IEEE Transactions on Information F orensics and Security, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:37.005195Z

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 515d030c-d2f7-43cc-8500-4bc204b49395 · outbound

This paper cites Deep learning-based counter anti- forensic of gan-based attack in hevc compressed domain using coding pattern analysis.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Deep learning-based counter anti- forensic of gan-based attack in hevc compressed domain using coding pattern analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:36.762771Z

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 9599ff7c-4c3e-430a-8166-649e87e99c72 · outbound

This paper cites Fabsoften: Face beautification via dynamic skin smoothing, guided feathering, and texture restoration.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Fabsoften: Face beautification via dynamic skin smoothing, guided feathering, and texture restoration

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:36.503474Z

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 70f91208-f92e-4137-8516-9b766bc6a626 · outbound

This paper cites Towards real-world blind face restoration with generative facial prior.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Towards real-world blind face restoration with generative facial prior

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T22:48:35.858417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a287fe40-ff1c-48ac-ac75-0cd8853ba4ca · outbound

This paper cites Ucf: Uncovering common features for generalizable deepfake detection.

Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement Ucf: Uncovering common features for generalizable deepfake detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:48:36.187257Z

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.

Pith citing papers

Observation 4730257e-e623-4a6c-a781-84eca59e8500 · inbound

Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning cites this paper.

Uncertainty-Aware Deepfake Detection via Multi-View Structural Learning Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T00:29:28.216754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.