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Paper Citation Record · LEDGER

Practical Manipulation Model for Robust Deepfake Detection

As of 19 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 2026-08-18T06:34:40.430872+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

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

source=pdf_text observed=2026-08-07T10:31:25.608149Z digest=sha256:40aafa58397feec7161b79efa3b6f92d728ca56df617c2f42efc638813392b0c

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

source=pdf_text observed=2026-08-07T10:31:25.612002Z digest=sha256:d437e287b393ac261cc44e81db99e3b6bb1218021921191fce0918d88264956f

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=sha256:7154697250df59411554da3276955362d417ae22ebd437208faa8692820d8229

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:f645e4cacce295707a27b6ae50d0ee2fe549f4e1a29a505d71bbab3dee804455

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:31:25.621423Z digest=sha256:dd1700bbdd6ab5aa650d7d16530395dd32716c09b1aa03e1bf291499a264c3c2

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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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T10:31:25.624226Z digest=sha256:ee9e54566d21eb559416b0ed0a043c912c1305e089ecf9ea0399393dd0c4f12c

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:d49b073b12ca5e5a7d171398a7a9afa6ddf508014697134bcb277706f5506564

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:ddd087a0f8d8f89a9c0fdd0f9f611a837730c90a38689408eca7444b567a67c1

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

source=pdf_text observed=2026-08-07T10:31:25.634025Z digest=sha256:746111ed59b075e08075b40ba88ab9d6ecec45358f3e919611f308b22b51a67e

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:d10ca981478659367864b6de443fe0f0f0f732971a0e3f150184a4502b2ad0fc

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=sha256:89e3d2052966fc30f54d336a4c4b569b75a08f3d4d8d36a30118ed20056186ba

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

source=pdf_text observed=2026-08-07T10:31:25.643614Z digest=sha256:c94ae6f072dc0df6a33cfa4810f500fc20901bb1fea9e3cf0da54eac4d498483

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

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

source=pdf_text observed=2026-08-07T10:31:25.649830Z digest=sha256:ae046208ff42116c9b28e1f6b67917f8f7e69de38ace05c386a682421e8ad77c

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

source=pdf_text observed=2026-08-07T10:31:25.652609Z digest=sha256:f7af0f95f90152fc9a6be16fc6d40988ac7bc92f0793c1617bd8b1ba5e1522c4

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=sha256:01df3edf78f817cda37fed669627af11c20e0af81df8a03520c4b2c1aa7b95fe

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

source=pdf_text observed=2026-08-07T10:31:25.661814Z digest=sha256:c9e64ce54c3abe7c722589315feeeac591db6ca22043a06fe96aa4a0b6dfcb8d

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

source=pdf_text observed=2026-08-07T10:31:25.667828Z digest=sha256:bbc897b5bdc83ba240ae6b6d5b5f4447ee59b46836f504ec0f6cd7aa93eceaed

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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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:c58a3a5a319f08122c052507e5a9b96b85b519f399b9947ecb6b615896032f22

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

source=pdf_text observed=2026-08-07T10:31:25.680339Z digest=sha256:4fbc5df55f45993ca91fe1efaa5f75ea4fed68ed2e411e5dfb9c45a8d9578bdf

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

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

source=pdf_text observed=2026-08-07T10:31:25.686439Z digest=sha256:910560887c977b9804bc942f94dbc5a1098948ac5cd98bf71aa047713f69832f

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=sha256:0ba6183996a30ed44c393ea5eca0b86cafaa55ef9152409777b7589ac19b57e6

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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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=2026-08-07T10:31:25.692611Z digest=sha256:47063c6b4d4f62cd4c6d5f7d6cad6066c336da7b8c52bf45cd6c17524829c95c

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

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=2026-08-07T10:31:25.695488Z digest=sha256:686d170fc1334b7d05fda7a3d8a58adce05054bbeabf36e170f2fdcd3ef7e70f

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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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=2026-08-07T10:31:25.698386Z digest=sha256:0761b3d770f4f783ca9ca1de6c10029734359bfacc08522a057d6668adf6f8f9

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:a394ba4c40812c8125c2a7f047d33b49aa1e6fe5462ef19a865679a53237f0d6

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

source=pdf_text observed=2026-08-07T10:31:25.704853Z digest=sha256:cf4a4c9ce5fc8ce0d6d0a27b59f7d41485827a071fd7b453e57cc168d4d47dff

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

source=pdf_text observed=2026-08-07T10:31:25.707645Z digest=sha256:fb68b45bfb70c001cab07e2412f69608b04a69f5f3fa8970b6d4740b15213a03

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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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=2026-08-07T10:31:25.710632Z digest=sha256:daa33ddada805cbdecae9d84465c297170bb453b87e0d855d85e3e6a3e257d72

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

source=pdf_text observed=2026-08-07T10:31:25.713339Z digest=sha256:47a97466ae2bae382b89ebf6e985d53ca843f49e9cad9f0bb9cd4d3d7140f035

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

source=pdf_text observed=2026-08-07T10:31:25.717167Z digest=sha256:5fa732234e8fba6366be2bda797a4054b2d123ef088178c59352ba8011ffa236

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=sha256:9e1fcf6e5182dd86ee87a275ab088d9a4fbbd46cec5cdef34dcc967698a1858d

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=sha256:89f2f5c561997924fe6e6cdd11cbd0c9a658451e2a539f1cba90a12837e3372f

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

source=pdf_text observed=2026-08-07T10:31:25.726512Z digest=sha256:a3db5f0482076759a5326e95dbded85bb67b73f966e03fdab9af16c4e16cfbdd

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

source=pdf_text observed=2026-08-07T10:31:25.729274Z digest=sha256:8f5f964e4640a9efbde802fc6f2ea362116ccfcb59fa9d16af1d19c109593f39

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

source=pdf_text observed=2026-08-07T10:31:25.732194Z digest=sha256:85ff1d11430268e374beee9a9dd5d2ed14dff4d63d53fe0038c4b7282dfa01da

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

source=pdf_text observed=2026-08-07T10:31:25.735033Z digest=sha256:953f17886bc12af7d92d9684efce6368d3ad3cdb0010832f9906951017b2c306

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

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

source=pdf_text observed=2026-08-07T10:31:25.738572Z digest=sha256:084c563b504e0acbf4777d581c51eb5b6393a55b7394dd49f1029700dc7e4a24

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=sha256:9f1fcabb7a07b3738b979fca91c7b1b409e242643d33eec644c3ae59867924cc

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

Resolution
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=sha256:25c70d1b88ffe632daf101c4fbb69bfc495b79131e3bea586a7d22a29d1affaa

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

source=pdf_text observed=2026-08-07T10:31:25.747809Z digest=sha256:cb75051c1ad8b86ac42fd2cb71a5a808c486d15fc6909bd6c9f24f27c577c690

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

source=pdf_text observed=2026-08-07T10:31:25.750671Z digest=sha256:ccdee6697e3efda4d4d067de59358ae89ce4cc17bf5619256d8854c8f9464e46

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=sha256:0a9dcb1966d428145d54219722ef88ccebdd18c193815b684d7f6369075d3d5c

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

source=pdf_text observed=2026-08-07T10:31:25.757706Z digest=sha256:5896320ed9e23f094c4344179eb24ef55f98f0d51f17e17b7d66aa19a316a690

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

source=pdf_text observed=2026-08-07T10:31:25.760837Z digest=sha256:2b159465d8146eb44eefc4155fd293d0d00012b5c6faf49412a9034d2390c082

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

source=pdf_text observed=2026-08-07T10:31:25.764070Z digest=sha256:e06e95cd4c8ba67cf66511a959e607df457311c68637bfe666595688e00b9a0e

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

source=pdf_text observed=2026-08-07T10:31:25.767278Z digest=sha256:8ee4712d4f4af0a1c6bb4ef073e787a28e20f5048e43aa87adee7c253b880dec

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

source=pdf_text observed=2026-08-07T10:31:25.770322Z digest=sha256:260cfc4ff09243f41a05943fc717b27c12fd41c4a881cc5c36d597dca729158f

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

source=pdf_text observed=2026-08-07T10:31:25.773854Z digest=sha256:19ad23653f4719fb98eb7bea0bb46bdeb3f6d1bef9d53ec29e354e541c3d2a60

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

source=pdf_text observed=2026-08-07T10:31:25.776944Z digest=sha256:b5ec549f9b9f718f90350e68414e0d1457d88ab3b61ed6134b2704a38ed3f2a1

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

source=pdf_text observed=2026-08-07T10:31:25.780150Z digest=sha256:f38da353ee8237840e3216893b08c5c4a621e9b4f37f09b9e0c8187d779f1fa7

Pith citing papers

No inbound Pith citation observations are available.