Paper Citation Record · LEDGER

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation

As of 11 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2506.05890.

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

pith.paper-citation-record.v1
2506.05890 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:44.636368Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T09:16:37.881212Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T07:52:13.672691Z

Reference resolution

63 of 63 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2f83a14-1a38-4827-a901-d0dab17856a9 · outbound

This paper cites Open- domain, content-based, multi-modal fact-checking of out- of-context images via online resources.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Open- domain, content-based, multi-modal fact-checking of out- of-context images via online resources

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:59.062729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 60c62935-ceee-4f73-b3e8-72de03e1a8a3 · outbound

This paper cites Exposing the deception: Uncover- ing more forgery clues for deepfake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Exposing the deception: Uncover- ing more forgery clues for deepfake detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.706920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 42e2ac0f-08f3-4cdc-829e-6d357210cbd7 · outbound

This paper cites Aligned and non-aligned double jpeg detection using convolutional neural networks.Jour- nal of Visual Communication and Image Representation, 49: 153–163, 2017.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Aligned and non-aligned double jpeg detection using convolutional neural networks.Jour- nal of Visual Communication and Image Representation, 49: 153–163, 2017

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.442126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d0a3c8e5-0c5a-4ba2-8743-a643fbf60faf · outbound

This paper cites Audio-visual person-of-interest deep- fake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Audio-visual person-of-interest deep- fake detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:58.120854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ad2d48c9-c86e-4d21-b8db-fa344cb04b23 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:37.831155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d95b32e-1f85-4866-b4f6-b7c4b9f5b0cc · outbound

This paper cites An empirical study of training end-to-end vision-and-language transformers.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation An empirical study of training end-to-end vision-and-language transformers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.823323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 58b722c5-067f-46dc-8c29-22bbdbc0562d · outbound

This paper cites Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020

Reference 7

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Source-reported events for the cited work

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Observation 1185772c-8877-4b83-a132-30e03275b865 · outbound

This paper cites Delving into the local: Dynamic in- consistency learning for deepfake video detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Delving into the local: Dynamic in- consistency learning for deepfake video detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.485540Z

Source-reported events for the cited work

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Observation c696d886-90a5-4f49-965f-b9d4a04a3e7c · outbound

This paper cites Deep residual learning for image recognition.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Deep residual learning for image recognition

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:38.475145Z

Source-reported events for the cited work

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Observation 6000fa62-6f22-4acf-932e-9351cf837ce9 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Momentum contrast for unsupervised visual rep- resentation learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:38.579509Z

Source-reported events for the cited work

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Observation 4c4a0c62-ec3a-4e68-86d8-103aebb361e7 · outbound

This paper cites Detection of fake images via the ensemble of deep representations from multi color spaces.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Detection of fake images via the ensemble of deep representations from multi color spaces

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:57.148313Z

Source-reported events for the cited work

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Observation d23ec748-075b-42b7-9909-793e446e71ab · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:38.952761Z

Source-reported events for the cited work

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Observation a5a54910-9491-44c9-a7a3-9e26b20fe89b · outbound

This paper cites Fighting fake news: Image splice detection via learned self-consistency.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Fighting fake news: Image splice detection via learned self-consistency

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:39.097255Z

Source-reported events for the cited work

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Observation 27364305-05c9-4d16-94cf-dd41334d18ea · outbound

This paper cites Bihpf: Bilateral high- pass filters for robust deepfake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Bihpf: Bilateral high- pass filters for robust deepfake detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:56.793365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation afdc16d7-8c86-4166-ae62-cccfc2119eaf · outbound

This paper cites Multimodal fusion with recurrent neural networks for rumor detection on microblogs.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Multimodal fusion with recurrent neural networks for rumor detection on microblogs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:56.320475Z

Source-reported events for the cited work

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Observation 4ee5331d-2816-4c4e-9b47-71a1ad43db46 · outbound

This paper cites Countering malicious deepfakes: Survey, battleground, and horizon.International journal of computer vision, 130(7):1678–1734, 2022.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Countering malicious deepfakes: Survey, battleground, and horizon.International journal of computer vision, 130(7):1678–1734, 2022

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:55.992383Z

Source-reported events for the cited work

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Observation 0422e08a-b539-4045-8ba7-b218564626c2 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:55.636659Z

Source-reported events for the cited work

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Observation d4f3a157-cbcf-4bc3-8c6d-b537b34cd1ed · outbound

This paper cites Mvae: Multimodal variational autoencoder for fake news detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Mvae: Multimodal variational autoencoder for fake news detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:55.237008Z

Source-reported events for the cited work

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Observation 9f0607bc-c543-4ac5-82f4-61adc4829e22 · outbound

This paper cites Vilt: Vision- and-language transformer without convolution or region su- pervision.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Vilt: Vision- and-language transformer without convolution or region su- pervision

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:39.799083Z

Source-reported events for the cited work

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Observation 239427a4-4f94-4b89-9bc5-40179039fc5b · outbound

This paper cites Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Align before fuse: Vision and language representation learn- ing with momentum distillation.Advances in neural infor- mation processing systems, 34:9694–9705, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:54.808047Z

Source-reported events for the cited work

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Observation 4cd99fa1-cb0d-493e-b010-06fec3a461ba · outbound

This paper cites Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:39.979138Z

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Observation ff95316e-3534-4086-bbc8-3a722dce76c8 · outbound

This paper cites Towards multimodal dis- information detection by vision-language knowledge inter- action.Information Fusion, 102:102037, 2024.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Towards multimodal dis- information detection by vision-language knowledge inter- action.Information Fusion, 102:102037, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:54.445033Z

Source-reported events for the cited work

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Observation 9355d2e6-4487-4009-a35f-15e3bb31cd4e · outbound

This paper cites Unified frequency-assisted trans- former framework for detecting and grounding multi-modal manipulation.International Journal of Computer Vision, pages 1–18, 2024.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Unified frequency-assisted trans- former framework for detecting and grounding multi-modal manipulation.International Journal of Computer Vision, pages 1–18, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:54.105717Z

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Observation c920d64e-cdda-4e64-aed0-08eb777b9c96 · outbound

This paper cites Fka-owl: Ad- vancing multimodal fake news detection through knowledge- augmented lvlms.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Fka-owl: Ad- vancing multimodal fake news detection through knowledge- augmented lvlms

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:53.880072Z

Source-reported events for the cited work

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Observation 4c5d42fa-e832-4903-91ca-18e4a75ff05b · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:40.387139Z

Source-reported events for the cited work

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Observation 0b29f08c-bcfd-4cbd-aae0-749cdad8f362 · outbound

This paper cites Decoupled Weight Decay Regularization.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Decoupled Weight Decay Regularization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:40.495276Z

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Observation eac1e57d-cd67-43e9-8f44-19b19e01545e · outbound

This paper cites NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:40.532883Z

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Observation ae2dbec0-0502-470d-9265-2537b94e76b2 · outbound

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

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Gener- alizing face forgery detection with high-frequency features

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:40.621841Z

Source-reported events for the cited work

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Observation 199439c5-c9b7-4fee-9128-153fdd84d8db · outbound

This paper cites Forensic similarity for digital images.IEEE Transactions on Information Forensics and Security, 15:1331–1346, 2019.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Forensic similarity for digital images.IEEE Transactions on Information Forensics and Security, 15:1331–1346, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:53.536049Z

Source-reported events for the cited work

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Observation 9ccabdf8-cae1-48a7-b544-6a7346f70b67 · outbound

This paper cites Exposing fake images with forensic similarity graphs.IEEE Journal of Selected Topics in Signal Processing, 14(5):1049–1064, 2020.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Exposing fake images with forensic similarity graphs.IEEE Journal of Selected Topics in Signal Processing, 14(5):1049–1064, 2020

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:53.190529Z

Source-reported events for the cited work

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Observation a1d086f7-0a36-404c-be13-806965adc08c · outbound

This paper cites Detecting gan- generated imagery using saturation cues.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Detecting gan- generated imagery using saturation cues

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:52.921658Z

Source-reported events for the cited work

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Observation d29bd27b-4214-4c47-bc32-9bb90d755c0b · outbound

This paper cites Hierarchical frequency-assisted interactive networks for face manipulation detection.IEEE Transac- tions on Information Forensics and Security, 17:3008–3021,.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Hierarchical frequency-assisted interactive networks for face manipulation detection.IEEE Transac- tions on Information Forensics and Security, 17:3008–3021,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:52.625585Z

Source-reported events for the cited work

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Observation 50e3180f-ae26-4384-8b97-743a4f0dc78c · outbound

This paper cites F 2 trans: High-frequency fine-grained transformer for face forgery detection.IEEE Transactions on Information Forensics and Security, 18:1039–1051, 2023.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation F 2 trans: High-frequency fine-grained transformer for face forgery detection.IEEE Transactions on Information Forensics and Security, 18:1039–1051, 2023

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:52.316311Z

Source-reported events for the cited work

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Observation 38cfb14e-fb75-42a3-ba0d-c21f5bb0ac4e · outbound

This paper cites Self-supervised distilled learning for multi-modal mis- information identification.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Self-supervised distilled learning for multi-modal mis- information identification

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:52.036092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc997ab0-0ad1-4fda-9419-250d41568d1b · outbound

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

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Laa-net: Localized artifact attention network for quality-agnostic and generalizable deepfake de- tection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:51.710322Z

Source-reported events for the cited work

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Observation 51f35dd8-4296-4669-b3bd-d9ae3f0273b4 · outbound

This paper cites Capsule-forensics: Using capsule networks to detect forged images and videos.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Capsule-forensics: Using capsule networks to detect forged images and videos

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:51.358053Z

Source-reported events for the cited work

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Observation e98aec79-ff82-4d2e-b9f5-af43548ddf1b · outbound

This paper cites Avff: Audio-visual feature fusion for video deepfake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Avff: Audio-visual feature fusion for video deepfake detection

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:51.072219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7432cb68-bdd2-49ca-80d5-6f89e62cdaab · outbound

This paper cites Deepfake generation and detection: A benchmark and survey.arXiv preprint arXiv:2403.17881,.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Deepfake generation and detection: A benchmark and survey.arXiv preprint arXiv:2403.17881,

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:41.581303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5526bf16-58e2-4781-8175-0b499c0c639e · outbound

This paper cites Deepfake text detec- tion: Limitations and opportunities.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Deepfake text detec- tion: Limitations and opportunities

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:50.734399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21070ce8-15bb-4b43-ac06-b934df56e9d4 · outbound

This paper cites Thinking in frequency: Face forgery detection by min- ing frequency-aware clues.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Thinking in frequency: Face forgery detection by min- ing frequency-aware clues

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:50.444620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a1b8a5d-a546-4fdd-9638-f1e14f4ad041 · outbound

This paper cites Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Language models are unsu- pervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:41.933471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4280bb5b-4405-4b2e-80a8-a4272de1ec33 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Learning transferable visual models from natural language supervi- sion

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:42.060265Z

Source-reported events for the cited work

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Observation a65bb5da-e787-4581-aeef-0fc3f30bb119 · outbound

This paper cites Detecting and grounding multi-modal media manipulation.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Detecting and grounding multi-modal media manipulation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:50.139866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e26c4480-7a02-4fc2-af9f-6ac5c329770d · outbound

This paper cites Detecting and grounding multi-modal media manip- ulation and beyond.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Detecting and grounding multi-modal media manip- ulation and beyond.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:49.907688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 48c38a3b-9e2b-47c5-9ad7-876da3e9ac45 · outbound

This paper cites Learning on gradients: Generalized arti- facts representation for gan-generated images detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Learning on gradients: Generalized arti- facts representation for gan-generated images detection

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:42.357691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef2c1f0-61b1-4d8c-b6cf-a415e2b5b243 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:49.673865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7a615101-fa26-4e6a-89b1-05a6c96d7887 · outbound

This paper cites Exploiting modality- specific features for multi-modal manipulation detection and grounding.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Exploiting modality- specific features for multi-modal manipulation detection and grounding

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:49.325867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f7176c0-c327-4fc8-827a-8c41dbe46276 · outbound

This paper cites Noise based deepfake de- tection via multi-head relative-interaction.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Noise based deepfake de- tection via multi-head relative-interaction

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:49.023150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 326a2965-0712-411b-9fe4-12fdafca56b0 · outbound

This paper cites Eann: Event adver- sarial neural networks for multi-modal fake news detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Eann: Event adver- sarial neural networks for multi-modal fake news detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:48.732082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fc0be62b-035a-444b-8160-7aa51b285d0d · outbound

This paper cites Add: Frequency attention and multi- view based knowledge distillation to detect low-quality com- pressed deepfake images.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Add: Frequency attention and multi- view based knowledge distillation to detect low-quality com- pressed deepfake images

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:48.430658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7b741265-4481-47ca-93d6-7de199e97dcc · outbound

This paper cites LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:43.063487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62262f53-224c-49e5-b1c8-5d01a559eab6 · outbound

This paper cites Unified contrastive learning in image-text-label space.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Unified contrastive learning in image-text-label space

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:48.101731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 30f1c272-6a9b-43ae-9a80-36dfb1db2384 · outbound

This paper cites Avoid-df: Audio-visual joint learning for detecting deepfake.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Avoid-df: Audio-visual joint learning for detecting deepfake

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:47.754647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e4680f1e-da14-4390-826b-12ee1fa9e873 · outbound

This paper cites Masked relation learning for deepfake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Masked relation learning for deepfake detection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:47.474493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2aa206df-576c-4f9d-8ae0-71d010a2d957 · outbound

This paper cites Dynamic differ- ence learning with spatio-temporal correlation for deepfake video detection.IEEE Transactions on Information Foren- sics and Security, 2023.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Dynamic differ- ence learning with spatio-temporal correlation for deepfake video detection.IEEE Transactions on Information Foren- sics and Security, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:47.152714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 263db666-2511-498c-8cbb-f5d60b35e574 · outbound

This paper cites Bootstrapping multi-view rep- resentations for fake news detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Bootstrapping multi-view rep- resentations for fake news detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:46.855527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8a294af9-a640-4eb0-9148-de8e545a14dc · outbound

This paper cites De- fending against neural fake news.Advances in neural infor- mation processing systems, 32, 2019.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation De- fending against neural fake news.Advances in neural infor- mation processing systems, 32, 2019

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:46.516032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b520f37a-c24b-4b7a-bdda-748ada87a893 · outbound

This paper cites Multi-attentional deep- fake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Multi-attentional deep- fake detection

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:46.227205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 22b4bfaf-ef36-44ba-880e-448eacddd51c · outbound

This paper cites Learning self-consistency for deepfake detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Learning self-consistency for deepfake detection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:46.051830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 23e5772a-3428-4d5f-958a-109a80eb4a4e · outbound

This paper cites A survey of deep facial attribute analysis.International Journal of Computer Vision, 128:2002–2034, 2020.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation A survey of deep facial attribute analysis.International Journal of Computer Vision, 128:2002–2034, 2020

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:45.826780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f908ef1d-926b-402d-bd9a-8e8a61762c57 · outbound

This paper cites Two-stream neural networks for tampered face detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Two-stream neural networks for tampered face detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:45.577925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 886156e6-c9fd-4dbd-b033-6973bf827b12 · outbound

This paper cites Multi-modal fake news detec- tion on social media via multi-grained information fusion.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Multi-modal fake news detec- tion on social media via multi-grained information fusion

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:45.374466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b4f8de7f-a2c3-41c9-8e12-b91bd2846310 · outbound

This paper cites Generalizing to the future: Mitigating entity bias in fake news detection.

Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation Generalizing to the future: Mitigating entity bias in fake news detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:18:45.085224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 05daabf8-1f49-4c32-ab61-1d6c0584ba93 · inbound

DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection cites this paper.

DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:52:13.673965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 54b0d352-79e3-4e54-8b40-06164f84c7f7 · inbound

Detecting AI-Generated Video: A Vision-Language Dual-View Survey cites this paper.

Detecting AI-Generated Video: A Vision-Language Dual-View Survey Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation

Reference 147

Resolution: unresolved; no resolver link, observed 2026-07-14T09:16:37.881212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.