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Paper Citation Record · LEDGER

Learning Counterfactually Decoupled Attention for Open-World Model Attribution

As of 7 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2506.23074.

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

pith.paper-citation-record.v1
2506.23074 v1

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

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measured 71 of 71 standing notices

One-hop event checks from named stored sources.

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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

71 of 71 outbound references displayed

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External citation measurements

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Outbound references

Observation b7fe6abb-8ef1-4622-ac1c-82129fe98235 · outbound

This paper cites com / iperov / DeepFaceLab.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution com / iperov / DeepFaceLab

Reference 1

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Observation 56b5d8a7-4f55-42f8-b1ff-e3e6079002b9 · outbound

This paper cites com / deepfakes / faceswap.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution com / deepfakes / faceswap

Reference 2

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Observation 357d7c57-74e8-4922-8dd1-10141a3cb1e0 · outbound

This paper cites SiT: Self-supervised vIsion Transformer.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution SiT: Self-supervised vIsion Transformer

Reference 3

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Observation bf89eee9-c647-4707-b8cd-228964d11dad · outbound

This paper cites Repmix: Represen- tation mixing for robust attribution of synthesized images.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Repmix: Represen- tation mixing for robust attribution of synthesized images

Reference 4

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Observation 699872ed-2b8d-420f-9a94-855deeab1058 · outbound

This paper cites Open-world semi-supervised learning.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Open-world semi-supervised learning

Reference 5

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Observation a40b012d-a3dc-42f1-9dd6-e82cde7ed44b · outbound

This paper cites Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation

Reference 6

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Observation 3abaaee8-2a0e-4306-baab-837cca155340 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Xception: Deep learning with depthwise separable convolutions

Reference 7

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Observation 7576df52-3fc5-475b-8f13-02f768f840c5 · outbound

This paper cites On the detection of digital face manipulation.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution On the detection of digital face manipulation

Reference 8

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Observation 4d62ace8-dbd2-4b83-9ab1-bdc0d45fcba9 · outbound

This paper cites Arcface: Additive angular margin loss for deep face recognition.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Arcface: Additive angular margin loss for deep face recognition

Reference 9

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Observation 4fff7638-bb5e-48e5-81f8-da09c17a0158 · outbound

This paper cites Reducing network agnostophobia.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Reducing network agnostophobia

Reference 10

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Observation 324bfb6c-ecc6-4e6c-b5f0-092208f628fa · outbound

This paper cites Diffusion mod- els beat gans on image synthesis.NeurIPS, 34:8780–8794,.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Diffusion mod- els beat gans on image synthesis.NeurIPS, 34:8780–8794,

Reference 11

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Observation 702c3de9-9ccc-46d9-8fd8-3e216a4bd29b · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Taming transformers for high-resolution image synthesis

Reference 12

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Observation 59f8e67c-3d1f-493c-9cd8-43533ecc5319 · outbound

This paper cites Leveraging fre- quency analysis for deep fake image recognition.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Leveraging fre- quency analysis for deep fake image recognition

Reference 13

Resolution
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Observation ed0b32ab-1e40-46a1-8578-0a3997792e12 · outbound

This paper cites Towards discovery and attribution of open-world gan generated images.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Towards discovery and attribution of open-world gan generated images

Reference 14

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Observation a348685f-2fc9-4de9-8919-59d872e5f8e8 · outbound

This paper cites Generative adversarial nets.NeurIPS, 27,.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Generative adversarial nets.NeurIPS, 27,

Reference 15

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Observation 17540bf0-dd8f-4493-8372-0e89099fb8bd · outbound

This paper cites On the exploitation of deepfake model recognition.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution On the exploitation of deepfake model recognition

Reference 16

Resolution
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Observation 6c87ac08-e50a-4c54-ae33-f752299a435b · outbound

This paper cites Robust semi-supervised learning when not all classes have labels.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Robust semi-supervised learning when not all classes have labels

Reference 17

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Observation 3622f50f-5902-4c29-b130-0c605ecbc1e3 · outbound

This paper cites Automatically discov- ering and learning new visual categories with ranking statis- tics.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Automatically discov- ering and learning new visual categories with ranking statis- tics

Reference 18

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Observation 4ccb6dbd-5693-4933-b33f-e370e6771053 · outbound

This paper cites Ghostnet: More features from cheap opera- tions.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Ghostnet: More features from cheap opera- tions

Reference 19

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Observation 2b684e6d-e0f6-4671-a48e-3be6b57a01d1 · outbound

This paper cites Forgerynet: A versatile benchmark for comprehensive forgery analysis.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Forgerynet: A versatile benchmark for comprehensive forgery analysis

Reference 20

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Observation 235bf9fc-a726-42a3-83dd-37b659382208 · outbound

This paper cites Denoising dif- fusion probabilistic models.NeurIPS, 33:6840–6851, 2020.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Denoising dif- fusion probabilistic models.NeurIPS, 33:6840–6851, 2020

Reference 21

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Observation fd0bdffa-9eef-4b9e-843e-fa2650e23c78 · outbound

This paper cites Pfa-gan: Progressive face aging with gen- erative adversarial network.TIFS, 16:2031–2045, 2020.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Pfa-gan: Progressive face aging with gen- erative adversarial network.TIFS, 16:2031–2045, 2020

Reference 22

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Observation 169b16f2-f75c-4f9a-bbcc-17661d0f3fca · outbound

This paper cites Sc-fegan: Face editing gen- erative adversarial network with user’s sketch and color.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Sc-fegan: Face editing gen- erative adversarial network with user’s sketch and color

Reference 23

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Observation 67b4b3bd-26f0-4479-af02-c1ae61c43e5e · outbound

This paper cites Towards open world object de- tection.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Towards open world object de- tection

Reference 24

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Observation dda65986-a94c-4515-8121-b11c48c0cfda · outbound

This paper cites Progressive growing of gans for improved quality, stability, and variation.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Progressive growing of gans for improved quality, stability, and variation

Reference 25

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Observation 1732295a-cb28-4293-9841-6283edabd1ad · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution A style-based generator architecture for generative adversarial networks

Reference 26

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Observation 2bd953dd-73f1-4d07-9472-313a04eb5b89 · outbound

This paper cites Decentralized attribution of generative models.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Decentralized attribution of generative models

Reference 27

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Observation 1f553863-fd92-48e6-8752-5ede495e1ff7 · outbound

This paper cites Auto-encoding varia- tional bayes.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Auto-encoding varia- tional bayes

Reference 28

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Observation 17fb892f-9343-43da-af59-ef269a45ade8 · outbound

This paper cites The hungarian method for the assignment problem.NRL, 2(1-2):83–97, 1955.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution The hungarian method for the assignment problem.NRL, 2(1-2):83–97, 1955

Reference 29

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Observation 168a12f4-b232-4241-9fe7-d621fd2123a2 · outbound

This paper cites Are handcrafted filters helpful for attributing ai-generated images? InACM MM, pages 10698–10706, 2024.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Are handcrafted filters helpful for attributing ai-generated images? InACM MM, pages 10698–10706, 2024

Reference 30

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Observation ec3d932f-2d68-438c-938c-2595792a5068 · outbound

This paper cites FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

Reference 31

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Observation 33e643e6-fc2b-49a6-80cc-e5dd14a80d2c · outbound

This paper cites Face x-ray for more general face forgery detection.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Face x-ray for more general face forgery detection

Reference 32

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Source-reported events for the cited work

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Observation 6096a439-84e0-4d67-b3eb-7272409bf4a9 · outbound

This paper cites Counterfactual intervention feature transfer for visible- infrared person re-identification.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Counterfactual intervention feature transfer for visible- infrared person re-identification

Reference 33

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Observation 442400d4-c8ee-4a6c-a909-06db057d2f4b · outbound

This paper cites Celeb-df: A large-scale challenging dataset for deep- fake forensics.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Celeb-df: A large-scale challenging dataset for deep- fake forensics

Reference 34

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T21:55:11.038646Z digest=sha256:cc7013b531b09557fc358c924569620952b3221c83f9eebc62c1f93f51ca3bda

Observation 44ac51a2-69e7-433c-a2b5-9a3df246a305 · outbound

This paper cites Detecting generated images by real images.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Detecting generated images by real images

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:20.339699Z

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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 265653bd-c668-49cc-a834-000697fda67e · outbound

This paper cites Which model generated this image? a model- agnostic approach for origin attribution.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Which model generated this image? a model- agnostic approach for origin attribution

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:20.170884Z

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=2026-08-06T21:55:11.217442Z digest=sha256:12f3c2f906137007b8d89277065b186ba82bf51b88b7e7285031e1de4f6f2e33

Observation 0daa1473-bef3-42d9-857b-701a7d67e546 · outbound

This paper cites Residual denoising diffu- sion models.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Residual denoising diffu- sion models

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:20.024000Z

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=2026-08-06T21:55:11.267760Z digest=sha256:5ed89c1b94d8abb6942e72d5ac6787aac3f3046c805ddc0ffcda80e3baa3d254

Observation 0ae2866c-b28f-4457-b2e1-4b6dd8fcd15e · outbound

This paper cites Discovering causal signals in images.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Discovering causal signals in images

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.820450Z

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=2026-08-06T21:55:11.395210Z digest=sha256:24bc77e09869f39a584f9e0dd5628770c06e91e25eac6ed03647a76bb919d522

Observation d77b088f-558c-4ff9-b201-a667118b27bb · outbound

This paper cites Do gans leave artificial fingerprints? In MIPR, pages 506–511, 2019.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Do gans leave artificial fingerprints? In MIPR, pages 506–511, 2019

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.687450Z

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=2026-08-06T21:55:11.452126Z digest=sha256:fe7403f0e35c15995ef51cf4764368b239d96b5adb57ba4989fca4d715ab686a

Observation 26eeac3e-cddb-4623-a83b-4b035c13a85a · outbound

This paper cites Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.505462Z

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=2026-08-06T21:55:11.504282Z digest=sha256:e9c0f55695c2a6ba56e54fec38eb1f7d28bc889cf6702be1bcc0d20912d95ad3

Observation 81b0571d-bcfe-4eed-96ca-846458575f84 · outbound

This paper cites FSGAN: Subject agnostic face swapping and reenactment.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution FSGAN: Subject agnostic face swapping and reenactment

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.397022Z

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=2026-08-06T21:55:11.590686Z digest=sha256:0442c86ba56a07cd6260675daf600fc840ee41c6c20e5cb3bfbff0c7c9d139fe

Observation 03902edd-4893-420a-b809-1d4430f02be4 · outbound

This paper cites Towards uni- versal fake image detectors that generalize across generative models.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Towards uni- versal fake image detectors that generalize across generative models

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.254375Z

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=2026-08-06T21:55:11.662091Z digest=sha256:49a1b129141e2efb379a60e75080f1f5e2a8c5b8f0a011d10c6c0cbfd28e1862

Observation db6039fe-a3e7-4a60-8b87-c787e75a439e · outbound

This paper cites Basic books, 2018.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Basic books, 2018

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:19.097035Z

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=2026-08-06T21:55:11.718905Z digest=sha256:a6f2dfd85d62ee2c3c346ca5e422afd8b384b05974f4f07d18339e76f2c102ce

Observation 24d3ad6d-83c0-4fe0-bd9c-edc5bde51161 · outbound

This paper cites Scalable diffusion models with transformers.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Scalable diffusion models with transformers

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.909961Z

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=2026-08-06T21:55:11.791445Z digest=sha256:b7b5b60673769db1a3962bf5628c06d0559c3c4f597fc26da812c551cf74d9ef

Observation a7dd7439-9e52-4141-99b8-3618a907aed7 · outbound

This paper cites Counterfactual attention learning for fine-grained visual cat- egorization and re-identification.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Counterfactual attention learning for fine-grained visual cat- egorization and re-identification

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.750116Z

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=2026-08-06T21:55:11.886038Z digest=sha256:c42f4432b8e5bc41aab8751280233341ca791fe15c3d125c87c13b074da3731f

Observation 8e190531-9752-4dc7-bedf-839c2f95adb4 · outbound

This paper cites Openldn: Learn- ing to discover novel classes for open-world semi-supervised learning.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Openldn: Learn- ing to discover novel classes for open-world semi-supervised learning

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.604721Z

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=2026-08-06T21:55:12.023187Z digest=sha256:42bbdd794275ec01c5414e644bad56745d445fccb9650345ec35d8dc7518cde7

Observation ca04b4b0-bcda-4342-855e-8408d12c3842 · outbound

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

Learning Counterfactually Decoupled Attention for Open-World Model Attribution High-resolution image syn- thesis with latent diffusion models

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Resolution
unresolved
no resolver link, observed 2026-08-06T21:55:12.136547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:55:12.136547Z digest=sha256:67956c198fd29f504bef9773b07c41624a36e399ec1c0832b7c38c527aefb969

Observation e0864022-47b6-470a-9ccd-8b8147fad6f5 · outbound

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

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Faceforen- sics++: Learning to detect manipulated facial images

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.425616Z

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=2026-08-06T21:55:12.291040Z digest=sha256:660436614761134e3c3059eda8f42bb5422e2173b8b6328debffd4e4e2f2c901

Observation 55413348-8be2-4e28-8dce-a5b37625af67 · outbound

This paper cites Open-world semantic segmen- tation including class similarity.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Open-world semantic segmen- tation including class similarity

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.270786Z

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=2026-08-06T21:55:12.434482Z digest=sha256:659741cd53807c685d36452923d64040300aebdbf7621114fad45c720875228f

Observation 960bb34f-eb6b-4e0b-9daa-4edf93701157 · outbound

This paper cites Learning structured output representation using deep conditional gen- erative models.NeurIPS, 28, 2015.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Learning structured output representation using deep conditional gen- erative models.NeurIPS, 28, 2015

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:18.064461Z

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=2026-08-06T21:55:12.545270Z digest=sha256:ae3ebb3571fccdb2a46d8c519d6335203b29c2e98b163406aa0326ddd909afe1

Observation ea1202a9-ecee-41d1-814e-dcb7f2877e72 · outbound

This paper cites Denois- ing diffusion implicit models.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Denois- ing diffusion implicit models

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Resolution
unresolved
no resolver link, observed 2026-08-06T21:55:12.671501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:55:12.671501Z digest=sha256:13a00510e0e2a83ab69cce342599f7a227088d7a61fe3aa372298351c38a761d

Observation 6fb250c5-80f1-4931-a8e7-88f80542ddcd · outbound

This paper cites Contrastive pseudo learning for open-world deepfake attribution.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Contrastive pseudo learning for open-world deepfake attribution

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:17.937995Z

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=2026-08-06T21:55:12.771543Z digest=sha256:e759fe99d5d3bc511e30669cf34099c115e4ffd881c36a56ac0a7ebcc47e830b

Observation 59ea4e36-c2fd-4261-8c30-8be590bbc011 · outbound

This paper cites Rethinking open-world deepfake attribution with multi-perspective sensory learning.IJCV, pages 1–24, 2024.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Rethinking open-world deepfake attribution with multi-perspective sensory learning.IJCV, pages 1–24, 2024

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Resolution
verified fuzzy
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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=2026-08-06T21:55:12.900818Z digest=sha256:2ac070167a9b3fac9eeadd31bc6595760a7cb7f31675650f5499de1402ee9790

Observation c938b217-3469-4bf8-9a50-ffabab114419 · outbound

This paper cites Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:17.566950Z

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=2026-08-06T21:55:13.011276Z digest=sha256:70d71b4926bda076147c87094bae6ec1177a1ba93169ee8fdd5ce66e38a43e76

Observation e80b36b7-3a71-4c5a-9c9d-a2657060a51a · outbound

This paper cites Visual autoregressive modeling: Scalable image gen- eration via next-scale prediction.NeurIPS, 37:84839–84865,.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Visual autoregressive modeling: Scalable image gen- eration via next-scale prediction.NeurIPS, 37:84839–84865,

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Resolution
unresolved
no resolver link, observed 2026-08-06T21:55:13.149053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:55:13.149053Z digest=sha256:9423aa96b8e567bef42f1399b43c94a36d6b2dbdbd5160b53ee079089ac38239

Observation 552b7148-a6c2-4fdd-a68c-2bbd2cd8e22a · outbound

This paper cites Neural discrete representation learning.NeurIPS, 30, 2017.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Neural discrete representation learning.NeurIPS, 30, 2017

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:17.415606Z

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=2026-08-06T21:55:13.308949Z digest=sha256:344eade4ba34d2359867366e91823c894bd8f77ea3fd7a545ab26178d92db53f

Observation 26e8ac1a-b090-416f-8079-854eb9e4a968 · outbound

This paper cites Visualizing data using t-sne.JMLR, 9(11), 2008.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Visualizing data using t-sne.JMLR, 9(11), 2008

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:17.257153Z

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=2026-08-06T21:55:13.402576Z digest=sha256:58524374bddd1dc629c2bda264ade26c8e32bcc912302635d201147ec35ce871

Observation fbebfaa2-ec00-4a38-aa9f-171b0a11f7c6 · outbound

This paper cites Counterfactual cycle-consistent learn- ing for instruction following and generation in vision- language navigation.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Counterfactual cycle-consistent learn- ing for instruction following and generation in vision- language navigation

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:17.078694Z

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=2026-08-06T21:55:13.466953Z digest=sha256:ed705932163a45faf9e0746ad897ac558fb100ec36c176570ab2b370d6dcb239

Observation 900e01a2-113b-47fb-939d-d4362dc21be6 · outbound

This paper cites Visual commonsense r-cnn.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Visual commonsense r-cnn

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.940208Z

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=2026-08-06T21:55:13.565983Z digest=sha256:ca8f05b9e580c671a5bc794a0c1f7c1773cf1edaec34616da98bad4df46a8624

Observation 8bab137c-1d08-4f7a-95f2-ce462237c184 · outbound

This paper cites Forgerynir: deep face forgery and detec- tion in near-infrared scenario.TIFS, 17:500–515, 2022.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Forgerynir: deep face forgery and detec- tion in near-infrared scenario.TIFS, 17:500–515, 2022

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.848460Z

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=2026-08-06T21:55:13.628019Z digest=sha256:39ecc82792a0dc097d9587e3b2fc4dcaa7e86bc5c0df7773e401578ba3db5eaf

Observation 1a60cbe8-bc21-4392-a341-5493a1bc9189 · outbound

This paper cites Where did i come from? origin attribution of ai-generated images.NeurIPS, 36:74478–74500, 2023.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Where did i come from? origin attribution of ai-generated images.NeurIPS, 36:74478–74500, 2023

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.740725Z

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=2026-08-06T21:55:13.711768Z digest=sha256:fffd0a379fbc40b507302ecb6c542f961d375ad223bae7222850635697db143d

Observation 16ed33a6-fedd-4692-a208-5bbdac6bc124 · outbound

This paper cites Df40: Toward next- generation deepfake detection.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Df40: Toward next- generation deepfake detection

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.625404Z

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=2026-08-06T21:55:13.785961Z digest=sha256:d04d06528c084a70105b45731bede951b3c023ec964f8c9a4a56323c4095e5c9

Observation 7ed28e9c-fa3b-45f0-9906-c9647bbef336 · outbound

This paper cites Deepfake network architecture attribution.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Deepfake network architecture attribution

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.476964Z

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=2026-08-06T21:55:13.909208Z digest=sha256:28d17ae8cc0ef88e9e9d99e95336e03d42202096143635b574c93e7ffe0c0d67

Observation 23a6dd3f-0bfd-41db-a9bd-f45daf544866 · outbound

This paper cites Progressive open space expan- sion for open-set model attribution.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Progressive open space expan- sion for open-set model attribution

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.322672Z

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=2026-08-06T21:55:14.016997Z digest=sha256:fc0d019bf94ac08c0e8ab4bf4a5a469c65133a4886fbcbfad60aadb409f3df48

Observation 2ce0d751-cfa1-4454-9237-cc0d539c062b · outbound

This paper cites Attributing fake images to gans: Learning and analyzing gan fingerprints.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Attributing fake images to gans: Learning and analyzing gan fingerprints

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.172747Z

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=2026-08-06T21:55:14.144824Z digest=sha256:5da683bd26ccbeba671f28b35c13e2e917098d0c81a3a54ff74cdf7a9091b1fa

Observation 6df55b51-aa8d-46ca-9699-1b47a3dc5465 · outbound

This paper cites Artificial fingerprinting for generative models: Root- ing deepfake attribution in training data.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Artificial fingerprinting for generative models: Root- ing deepfake attribution in training data

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:16.007678Z

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=2026-08-06T21:55:14.217603Z digest=sha256:a07ce961e16369d35df6e0fd2b4aa13de2f4aa8d1f8f1681b2fff31a0c88fcb6

Observation 486bbc4e-5b23-400e-94ed-a67ec7c030c2 · outbound

This paper cites Responsible disclosure of generative mod- els using scalable fingerprinting.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Responsible disclosure of generative mod- els using scalable fingerprinting

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verified fuzzy
raw_fallback, observed 2026-08-06T21:55:15.793022Z

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=2026-08-06T21:55:14.348671Z digest=sha256:657f5204681718d54fc1dc830c6e162011e36e59bf487d52ac6b1d27d4d46e95

Observation 4dd8794e-93e2-4960-b9a5-bc0e52cc2821 · outbound

This paper cites Counterfactual zero-shot and open-set vi- sual recognition.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Counterfactual zero-shot and open-set vi- sual recognition

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:15.565784Z

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=2026-08-06T21:55:14.435987Z digest=sha256:181017d63e8677c4babd8b50e8e586d6a3f4f6fa395ad00420ecb63ed442604f

Observation 0b9a64ba-fcb7-45de-a8ca-bf61e04d5113 · outbound

This paper cites Multi-attentional deep- fake detection.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Multi-attentional deep- fake detection

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:15.405026Z

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=2026-08-06T21:55:14.503201Z digest=sha256:bb7b7d88600a4c3b1681d13b349dfc3fb375906663f7c6ff95e6ee96c7c68889

Observation 906f820d-1e5e-4e31-b269-269edbdf9da7 · outbound

This paper cites Learning deep features for discrimi- native localization.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Learning deep features for discrimi- native localization

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:15.187063Z

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=2026-08-06T21:55:14.605608Z digest=sha256:ef0777e90d94c095659e6c3179c2fb76164cc21223ae9bb758da9614ad453e12

Observation 6285a43a-c464-486b-bf44-e4001fdf06db · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networkss.

Learning Counterfactually Decoupled Attention for Open-World Model Attribution Unpaired image-to-image translation using cycle- consistent adversarial networkss

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:55:14.915457Z

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=2026-08-06T21:55:14.694995Z digest=sha256:65c1533a6f8fed5aa0e4f7c459796d0c55a827b339a3a07cf6070dcebd2338aa

Pith citing papers

No inbound Pith citation observations are available.