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Paper Citation Record · LEDGER

Practical Manipulation Model for Robust Deepfake Detection

As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2506.05119.

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

pith.paper-citation-record.v1
2506.05119 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:31:25.780150Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

56 of 56 outbound references displayed

  • verified exact2
  • verified fuzzy30
  • unresolved23
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a35ac7ab-9680-41c3-9a9e-a2e1976ebd4a · outbound

This paper cites Mesonet: a compact facial video forgery detection network.2018 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1–7, 2018.

Practical Manipulation Model for Robust Deepfake Detection Mesonet: a compact facial video forgery detection network.2018 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1–7, 2018

Reference 1

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.608149Z digest=sha256:d4ea8f6840f0e07f8be893397e6d2f6f39f6a20b89f97bb77ab257be41908984

Observation 203fb46d-047d-46b3-931d-0781b792bbcd · outbound

This paper cites Who inadvertently shares deepfakes? an- alyzing the role of political interest, cognitive ability, and social network size.Telematics and Informatics, 57:101508,.

Practical Manipulation Model for Robust Deepfake Detection Who inadvertently shares deepfakes? an- alyzing the role of political interest, cognitive ability, and social network size.Telematics and Informatics, 57:101508,

Reference 2

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.612002Z digest=sha256:28f8e5c6e0225ef492631e2affdbb00a58a1b4fedff28902c28536599f69f4e8

Observation 4e9b1281-7ead-4095-8f4c-b80f76bf055c · outbound

This paper cites The devil is in the details: Stylefeatureeditor for detail-rich stylegan inversion and high quality image editing,.

Practical Manipulation Model for Robust Deepfake Detection The devil is in the details: Stylefeatureeditor for detail-rich stylegan inversion and high quality image editing,

Reference 3

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source=pdf_text observed=2026-08-07T10:31:25.614999Z digest=sha256:7638d9deac15e96df819f8a01654baefa59f55b92e18328ba46295bae6575214

Observation 113d1aed-5a9e-475a-b568-34585cf660e2 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-07T10:31:25.618484Z digest=sha256:2e8d65365a4a5e855fc8a37ebb8e08ffc68b1e148eb2a50ef882966eeda0de9a

Observation e8806a0d-1687-4fc0-8a9c-917a4871b6dc · outbound

This paper cites End-to-end reconstruction- classification learning for face forgery detection.

Practical Manipulation Model for Robust Deepfake Detection End-to-end reconstruction- classification learning for face forgery detection

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.621423Z digest=sha256:f315f36e39289c59fde616b5887d4ca119bc0aca0fa6e6a9fcbbd99ec392cd9e

Observation 6f520307-b5ce-445c-82a4-2ef8650d2c4d · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1800–1807,.

Practical Manipulation Model for Robust Deepfake Detection Xception: Deep learning with depthwise separable convolutions.2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1800–1807,

Reference 6

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source=pdf_text observed=2026-08-07T10:31:25.624226Z digest=sha256:53fc4b3d60aad43dcb3bf271fca31f51e98fe0f8e2a140f14c952cce5bb2c5ef

Observation c7de6752-85b4-41ca-a6ba-e65df051ace3 · outbound

This paper cites Pillow (pil fork) documentation, 2015.

Practical Manipulation Model for Robust Deepfake Detection Pillow (pil fork) documentation, 2015

Reference 7

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source=pdf_text observed=2026-08-07T10:31:25.627252Z digest=sha256:58e4755a72442c6a4de16c98d6fb1c2b4712f163505c17feaf863031fb5ceef7

Observation d331df6d-2f8b-4a50-8c1b-4cdd3bf61b46 · outbound

This paper cites ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection.

Practical Manipulation Model for Robust Deepfake Detection ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection

Reference 8

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source=pdf_text observed=2026-08-07T10:31:25.630285Z digest=sha256:1440354883b39decedeae14fb2642a5324650b29fbce94f5c4384ecd9130d067

Observation 4204a975-3566-4816-a4a8-6514d74c6761 · outbound

This paper cites deepfakes faceswap.https://github.

Practical Manipulation Model for Robust Deepfake Detection deepfakes faceswap.https://github

Reference 9

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.634025Z digest=sha256:41898d17c817f67f8860e2f447b2b67ca55a49f8d640bcb7e8531b609f70a7ef

Observation 028d32b2-34da-4874-ba8f-ccb7e6ec854c · outbound

This paper cites The Deepfake Detection Challenge (DFDC) Preview Dataset.

Practical Manipulation Model for Robust Deepfake Detection The Deepfake Detection Challenge (DFDC) Preview Dataset

Reference 10

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source=pdf_text observed=2026-08-07T10:31:25.636883Z digest=sha256:4316d8fc0acdf252621cefb9c6b4e69cd0a1cfda277f8432910500e7c64db47e

Observation 222b7063-4ba4-4eee-b77f-71b6b478dd24 · outbound

This paper cites The DeepFake Detection Challenge (DFDC) Dataset.

Practical Manipulation Model for Robust Deepfake Detection The DeepFake Detection Challenge (DFDC) Dataset

Reference 11

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source=pdf_text observed=2026-08-07T10:31:25.640179Z digest=sha256:15ff14e623a564da8a113653c357699649da861ab3aeee2cc6423ec44a1e70b8

Observation 871ed51e-4570-4820-8c27-d73e32ddc059 · outbound

This paper cites Donald trump with boris johnson’s haircut.

Practical Manipulation Model for Robust Deepfake Detection Donald trump with boris johnson’s haircut

Reference 12

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.643614Z digest=sha256:57cac8bdcce8bd849227725232934ce80176ada8439af013cf182d06963515fb

Observation f8b73f11-2bba-4915-9726-aa9a10791ed7 · outbound

This paper cites How ai tools fueled online conspiracy theo- ries after trump assassination attempt.https://dfrlab.

Practical Manipulation Model for Robust Deepfake Detection How ai tools fueled online conspiracy theo- ries after trump assassination attempt.https://dfrlab

Reference 13

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.646399Z digest=sha256:88450046dc721c83423cec4b02d29f3673cdc17174baa809c43eeaecaa1a9267

Observation df9645f6-9a2e-4933-b408-eb59ce5e2721 · outbound

This paper cites Controllable guide-space for generalizable face forgery detection.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 20761–20770, 2023.

Practical Manipulation Model for Robust Deepfake Detection Controllable guide-space for generalizable face forgery detection.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 20761–20770, 2023

Reference 14

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.649830Z digest=sha256:27aa7d8e86f73e776ebe0b8a6ae731c12cca9bb688cb27eeaa9ba1a6fd8fdc99

Observation 73569010-936b-49c8-8246-3116e555d8af · outbound

This paper cites The social im- pact of deepfakes, 2021.

Practical Manipulation Model for Robust Deepfake Detection The social im- pact of deepfakes, 2021

Reference 15

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 44456ff5-a3c4-42a4-949b-458562341608 · outbound

This paper cites FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images.

Practical Manipulation Model for Robust Deepfake Detection FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images

Reference 16

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source=pdf_text observed=2026-08-07T10:31:25.655421Z digest=sha256:cfb63a78d972421ba9bcd7a53334fabb3dda493f8006ded3dd83e7acdb2bedb6

Observation e7dccb3b-456a-40b6-9f7c-857b461a444a · outbound

This paper cites Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis.

Practical Manipulation Model for Robust Deepfake Detection Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

Reference 17

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source=pdf_text observed=2026-08-07T10:31:25.658604Z digest=sha256:7b127b7f8cf054654e0efc8985e5600f6aec935323d0a132078de24cf4c01e6f

Observation c1cffdd0-0358-43c8-ab2a-f524f01c549a · outbound

This paper cites Deepvi- sion: Deepfakes detection using human eye blinking pattern.

Practical Manipulation Model for Robust Deepfake Detection Deepvi- sion: Deepfakes detection using human eye blinking pattern

Reference 18

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 3406ebff-9e94-46f4-bd49-326b0c18b194 · outbound

This paper cites Faceswap.https://github.com/ MarekKowalski/FaceSwap, 2018.

Practical Manipulation Model for Robust Deepfake Detection Faceswap.https://github.com/ MarekKowalski/FaceSwap, 2018

Reference 19

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f069dbac-8b37-402b-8a51-f564eb8c1e45 · outbound

This paper cites Seeable: Soft discrepancies and bounded contrastive learning for exposing deepfakes.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 20954–20964, 2022.

Practical Manipulation Model for Robust Deepfake Detection Seeable: Soft discrepancies and bounded contrastive learning for exposing deepfakes.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 20954–20964, 2022

Reference 20

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.667828Z digest=sha256:d94719058b95410db153bc63466d1232aab44581580265100f40bbde3a266369

Observation 14ceac88-0ce6-4987-a246-72941a66baaf · outbound

This paper cites Face x-ray for more general face forgery detection.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5000–5009, 2019.

Practical Manipulation Model for Robust Deepfake Detection Face x-ray for more general face forgery detection.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5000–5009, 2019

Reference 21

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source=pdf_text observed=2026-08-07T10:31:25.670849Z digest=sha256:443895c52fa342e4f64a8dc49005081c9e7fa163c2ebc4878d80e86f9ab963fc

Observation e76bc701-6a4d-4eb6-a293-433be1750294 · outbound

This paper cites Celeb-df: A large-scale challenging dataset for deep- fake forensics.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3204–3213,.

Practical Manipulation Model for Robust Deepfake Detection Celeb-df: A large-scale challenging dataset for deep- fake forensics.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3204–3213,

Reference 22

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source=pdf_text observed=2026-08-07T10:31:25.673940Z digest=sha256:ca6128b201acadb6be79a1e2bd028c41b81b36091b6cc75b9d7fd5a0945c6ff6

Observation 4bc33b42-04ab-4f4b-8d0d-2a3feeadd8d5 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-07T10:31:25.677353Z digest=sha256:fa58aeb0095e599121bcb999d7bd6a09aee18230a669703a4782d82311bfac01

Observation 6c3c4e26-73ba-4cd0-9b53-a0dd2f681574 · outbound

This paper cites A new approach to im- prove learning-based deepfake detection in realistic condi- tions, 2022.

Practical Manipulation Model for Robust Deepfake Detection A new approach to im- prove learning-based deepfake detection in realistic condi- tions, 2022

Reference 24

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.680339Z digest=sha256:81834ba29a8b370f9d217f388402e495c541567771b7f6a9eca2cb697654a339

Observation 3c947f21-e3e0-4af6-88a3-d8503aab1fc6 · outbound

This paper cites Assessment Framework for Deepfake Detection in Real-world Situations.

Practical Manipulation Model for Robust Deepfake Detection Assessment Framework for Deepfake Detection in Real-world Situations

Reference 25

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local_arxiv, observed 2026-08-07T10:31:25.963334Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.683169Z digest=sha256:3e634996dccb951b49b2bdf2f7005aca19d78fd25ce9bc0d33546e43e555fc35

Observation ee1b6223-7289-4dc1-96f8-fd209cca708f · outbound

This paper cites Gener- alizing face forgery detection with high-frequency features.

Practical Manipulation Model for Robust Deepfake Detection Gener- alizing face forgery detection with high-frequency features

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.686439Z digest=sha256:3bd48a000efa6acd2b51ee34a34379341782be0004bab92533584c17c1fe3b47

Observation 05427706-a658-4a89-9908-185c43ec50c8 · outbound

This paper cites Two-branch Recurrent Network for Isolating Deepfakes in Videos.

Practical Manipulation Model for Robust Deepfake Detection Two-branch Recurrent Network for Isolating Deepfakes in Videos

Reference 27

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.689195Z digest=sha256:e362dcc834d52c06dcfca4536fdd2e28aa701ce2ae067d3305bed32984bbf50b

Observation 715065e9-67c3-47ee-ad84-fa6178bfba2d · outbound

This paper cites Fbi names shooter after trump as- sassination attempt.https://www.moreechampion.

Practical Manipulation Model for Robust Deepfake Detection Fbi names shooter after trump as- sassination attempt.https://www.moreechampion

Reference 28

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raw_fallback, observed 2026-08-07T10:31:25.939685Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.692611Z digest=sha256:08a8238cdc00a1105142ad96b77d64318e855201d92b1969b1a5ddbbe14053a9

Observation 245b34e8-67f8-4678-ab99-08255167fbee · outbound

This paper cites Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake de- tection.

Practical Manipulation Model for Robust Deepfake Detection Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake de- tection

Reference 29

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raw_fallback, observed 2026-08-07T10:31:26.227463Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.695488Z digest=sha256:cb2ad21841fcc1226add5b068c5514f9a1a8236bb9320d75ec4b779df78822d3

Observation 6c3e1ac4-03c0-4ea4-bdd4-a53f659fcc25 · outbound

This paper cites Poisson image editing.ACM SIGGRAPH 2003 Papers, 2003.

Practical Manipulation Model for Robust Deepfake Detection Poisson image editing.ACM SIGGRAPH 2003 Papers, 2003

Reference 30

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raw_fallback, observed 2026-08-07T10:31:26.218084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.698386Z digest=sha256:c6bb6c463ec3e9fec7cc830bce88eb0c9fe0bd4464d82b7f996be71ab7f3f8af

Observation 75f756af-3ec5-4276-abdf-07fd4320f761 · outbound

This paper cites Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues.

Practical Manipulation Model for Robust Deepfake Detection Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues

Reference 31

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.701269Z digest=sha256:bd613f33320f028c0660a78285945d3d9710b2f740f3d8e70c1601161df462e3

Observation 7fc4c0ea-5cc5-42dc-b0a3-bbb6268612bb · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models, 2021.

Practical Manipulation Model for Robust Deepfake Detection High-resolution image syn- thesis with latent diffusion models, 2021

Reference 32

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raw_fallback, observed 2026-08-07T10:31:26.208686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.704853Z digest=sha256:748df2f5e03f5c93bf5781c8d56da4c1eeabcea583070431f67e3542e9dc4d23

Observation ca312fdf-9e0a-4e22-8dcb-bbcbc7360f7a · outbound

This paper cites FaceForen- sics++: Learning to detect manipulated facial images.

Practical Manipulation Model for Robust Deepfake Detection FaceForen- sics++: Learning to detect manipulated facial images

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.707645Z digest=sha256:636374334aeac52dbc61681a65eed19c4e57d0b17ca8d87bb53c5e7bbabd5804

Observation 42e7602d-d022-4846-94b1-42dad5e1c41f · outbound

This paper cites Natarajan.

Practical Manipulation Model for Robust Deepfake Detection Natarajan

Reference 34

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raw_fallback, observed 2026-08-07T10:31:26.189230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.710632Z digest=sha256:a98725dc63d585eb76073ecca68f7d53762eac9230a0fabc4ddfb8bda92e3d9c

Observation e2a49ff7-10dd-48a9-a9bd-e7e30efad7a7 · outbound

This paper cites Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Ba- tra.

Practical Manipulation Model for Robust Deepfake Detection Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Ba- tra

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.180042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.713339Z digest=sha256:b43379175b09dddfab907a79dafccbd06e71fc46963d29ce60eaa5ca8b03144c

Observation 71e65312-eb74-4c89-98a4-6513afad29aa · outbound

This paper cites Yamasaki.

Practical Manipulation Model for Robust Deepfake Detection Yamasaki

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.170779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.717167Z digest=sha256:c008e2eac1922e4f71876fd7840c7b53741d5884c7d0cf2143979c2a510398ff

Observation 1632e447-2706-4f4a-9820-9bb06082685d · outbound

This paper cites Intriguing properties of neural networks.

Practical Manipulation Model for Robust Deepfake Detection Intriguing properties of neural networks

Reference 37

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unresolved
no resolver link, observed 2026-08-07T10:31:25.720030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.720030Z digest=sha256:183ddc2faea39d80ad03c42be708a1f3fb9317c9d8458319924a95684fdec1ec

Observation 1a6aca98-4498-4136-8661-ea3d842c9a7e · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Practical Manipulation Model for Robust Deepfake Detection EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T10:31:25.723341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.723341Z digest=sha256:239594e6dd27535eb089a4f186fec23d9740d69d8d9b50423eb333c33fc46b0a

Observation 1533d1b9-5c71-4da6-974a-ee4dbff488fc · outbound

This paper cites Face2face: Real-time face capture and reenactment of rgb videos.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2387–2395, 2016.

Practical Manipulation Model for Robust Deepfake Detection Face2face: Real-time face capture and reenactment of rgb videos.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2387–2395, 2016

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.160676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.726512Z digest=sha256:d6693dbe3dac45abed7dbd83ceb3cff8bc2739b884d621734c723505239a3f91

Observation 727cca81-8931-404e-b3d3-80f4b8297b4c · outbound

This paper cites De- ferred neural rendering.ACM Transactions on Graphics (TOG), 38:1 – 12, 2019.

Practical Manipulation Model for Robust Deepfake Detection De- ferred neural rendering.ACM Transactions on Graphics (TOG), 38:1 – 12, 2019

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.151043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.729274Z digest=sha256:b01be1262114a2b1109a54caeed095bb1fdc531b764a7ae4b0c9649f23201253

Observation cc9b0ad4-74e3-4ed7-9c73-d21b3192f491 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:31:26.140444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.732194Z digest=sha256:d6e0a09a937bd90c1c4d1dd81739bcfa16fd0648b6438d87a674dbf64abd21f1

Observation 2a9bab7e-5944-4688-97b6-975f126ff2e9 · outbound

This paper cites Transcending forgery specificity with latent space augmentation for generalizable deepfake detection.

Practical Manipulation Model for Robust Deepfake Detection Transcending forgery specificity with latent space augmentation for generalizable deepfake detection

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.131135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.735033Z digest=sha256:1cfc23066c5d4d60407f688014ba610de507bc45d9844bf63d5880556e3d32d2

Observation 9ba06d64-e0f3-40ed-bf24-f29a24b5acb6 · outbound

This paper cites Ucf: Uncovering common features for generalizable deep- fake detection.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 22355–22366, 2023.

Practical Manipulation Model for Robust Deepfake Detection Ucf: Uncovering common features for generalizable deep- fake detection.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 22355–22366, 2023

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.120527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.738572Z digest=sha256:7c9b2516e7e56c3dded80bc99914fb51f5a0ae03abe79a541f0f59f24d8fa823

Observation e5fc4d4d-abe6-42a8-b157-1545d299e4e1 · outbound

This paper cites DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection.

Practical Manipulation Model for Robust Deepfake Detection DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

Reference 44

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unresolved
no resolver link, observed 2026-08-07T10:31:25.741646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.741646Z digest=sha256:f20054856e6512ee9d4043b32a9aa0a1a27732bdacd14b874ebf25b6290f593d

Observation aa47baec-f3c0-4aab-b757-c2c133ea6115 · outbound

This paper cites DF40: Toward Next-Generation Deepfake Detection.

Practical Manipulation Model for Robust Deepfake Detection DF40: Toward Next-Generation Deepfake Detection

Reference 45

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unresolved
no resolver link, observed 2026-08-07T10:31:25.744733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.744733Z digest=sha256:c90d1b42d2e41436ffeb4fe6addbc6413c0274b906944d67bdd156029ba5798e

Observation 30f41928-16dc-4795-9690-fa06cf58ccd6 · outbound

This paper cites Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte.

Practical Manipulation Model for Robust Deepfake Detection Zhang, Jingyun Liang, Luc Van Gool, and Radu Timofte

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.110330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.747809Z digest=sha256:a52437a6c1f593d7d3e5bb2b46adacbd8cbd1b529168bf0fa9020f53e2268f2c

Observation 94863247-cdab-4029-b8b9-04f24e2cc1ab · outbound

This paper cites Learning self-consistency for deep- fake detection.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15003–15013, 2020.

Practical Manipulation Model for Robust Deepfake Detection Learning self-consistency for deep- fake detection.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 15003–15013, 2020

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.100382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.750671Z digest=sha256:130ae68d0fd4f2b35ebd8e565dde3afd40913f13ec78304b3027ba7f5378c9c0

Observation 3c6ee288-8fee-4707-896d-5900f05904ae · outbound

This paper cites UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection.

Practical Manipulation Model for Robust Deepfake Detection UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection

Reference 48

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unresolved
no resolver link, observed 2026-08-07T10:31:25.753489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:31:25.753489Z digest=sha256:f37edada7ccf147baede6060f69ced719b26b6e850bbff3a3366e7ae41dad454

Observation 23c4f65d-0b79-47d0-86cb-c4bd98fcd36d · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 49

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unresolved
raw_fallback, observed 2026-08-07T10:31:26.090348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.757706Z digest=sha256:714de3c3247316940e77fc86280a558e026386a8a54774612b61de39a58d5282

Observation ee32840e-e2dd-4b77-bcaa-0a40d824e836 · outbound

This paper cites Positions outside of the image region result in the text being partially visible.

Practical Manipulation Model for Robust Deepfake Detection Positions outside of the image region result in the text being partially visible

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.080839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.760837Z digest=sha256:fd64c6ec2b0d85603c6c063620ca4bc11a5daf361459d9e5ae3abb8041f60fbd

Observation 1a8e02ef-62bd-4307-b023-3b0faffcfa85 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:31:26.070275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.764070Z digest=sha256:15ef775055c5889a2f5e8f8b15ce3b529021319d74aead3071f5c29945267b9a

Observation feefea4e-3643-4a47-9c89-42cfbecca476 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 52

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:31:26.059616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.767278Z digest=sha256:f677ba55a51a22f24d63adce72be671b93dff1dfb0c3a6cf9d867a1abd78e073

Observation 315b27bb-5059-42ed-826e-dcae7795957b · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:31:26.049847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.770322Z digest=sha256:c1fabf1fedc9184cd88e738e38076f95f842dac8e5f2285adee1afa3cf694ad8

Observation e5170d80-ae03-4a14-817b-32f3adc4e7d1 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:31:26.039431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.773854Z digest=sha256:891acc081fae8c8723f1a9c2e2ee4dfbaa0082006e6fa3240650ccbce23c250f

Observation fd841444-884c-4a1e-93cc-01337c46a2f8 · outbound

This paper cites an unresolved cited work.

Practical Manipulation Model for Robust Deepfake Detection Unresolved cited work

Reference 55

Resolution
malformed identifier
raw_fallback, observed 2026-08-07T10:31:26.029913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.776944Z digest=sha256:4d099175c590444a68e91bc41af937b9d72f85252763c17ec55c10596389115f

Observation f6478399-b110-4ae0-a51c-90be5668407b · outbound

This paper cites All images are taken from the Celeb- DF-v2 dataset [22].

Practical Manipulation Model for Robust Deepfake Detection All images are taken from the Celeb- DF-v2 dataset [22]

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:31:26.020280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T10:31:25.780150Z digest=sha256:0f46a5255b4247efa1007870388709187b6c1fb81de75d06295a2e245d0c137c

Pith citing papers

No inbound Pith citation observations are available.