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Paper Citation Record · LEDGER

RAISE: Realness Assessment for Image Synthesis and Evaluation

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2505.19233.

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

pith.paper-citation-record.v1
2505.19233 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:20:22.220810Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T20:06:44.440770Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:08:12.866069Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact0
  • verified fuzzy25
  • unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c9a85de-df70-4b6b-9dd6-029b569c4f95 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

RAISE: Realness Assessment for Image Synthesis and Evaluation High- resolution image synthesis with latent diffusion models,

Reference 1

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Observation 85468f1c-91d7-4918-97ae-28c5cffe3151 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

RAISE: Realness Assessment for Image Synthesis and Evaluation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 2

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Observation f646efa9-6ca3-43a6-b0b1-56d2db31cb56 · outbound

This paper cites Making a “completely blind.

RAISE: Realness Assessment for Image Synthesis and Evaluation Making a “completely blind

Reference 3

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Observation aacb4650-8aac-401d-a0ee-5be4ae255a0b · outbound

This paper cites NIMA: Neural image assessment,.

RAISE: Realness Assessment for Image Synthesis and Evaluation NIMA: Neural image assessment,

Reference 4

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Observation 24d96761-70a0-41ad-ac93-d0ea70af1e28 · outbound

This paper cites A V A: A large-scale database for aesthetic visual analysis,.

RAISE: Realness Assessment for Image Synthesis and Evaluation A V A: A large-scale database for aesthetic visual analysis,

Reference 5

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Source-reported events for the cited work

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Observation f2b85cc3-9181-4877-89cf-b55cad05c843 · outbound

This paper cites KonIQ-10k: An ecologi- cally valid database for deep learning of blind image quality assessment,.

RAISE: Realness Assessment for Image Synthesis and Evaluation KonIQ-10k: An ecologi- cally valid database for deep learning of blind image quality assessment,

Reference 6

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Observation 9729203f-eff5-4d92-b3ff-ed5ee411f573 · outbound

This paper cites A perceptual quality assessment exploration for AIGC images,.

RAISE: Realness Assessment for Image Synthesis and Evaluation A perceptual quality assessment exploration for AIGC images,

Reference 7

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 5168fe50-85e3-4d3d-b437-31312b9ec5d2 · outbound

This paper cites AGIQA-3k: An open database for AI-generated image quality assessment,.

RAISE: Realness Assessment for Image Synthesis and Evaluation AGIQA-3k: An open database for AI-generated image quality assessment,

Reference 8

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c2792f2d-e756-4843-9d43-27771c50b0d0 · outbound

This paper cites AIG- CIQA2023: A large-scale image quality assessment database for AI generated images: from the perspectives of quality, authenticity and correspondence,.

RAISE: Realness Assessment for Image Synthesis and Evaluation AIG- CIQA2023: A large-scale image quality assessment database for AI generated images: from the perspectives of quality, authenticity and correspondence,

Reference 9

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Observation 8c2f62bb-7133-4cf7-a8a7-5fba29351c2f · outbound

This paper cites Exploring the Naturalness of AI-Generated Images.

RAISE: Realness Assessment for Image Synthesis and Evaluation Exploring the Naturalness of AI-Generated Images

Reference 10

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Observation 271de9cd-2ab4-40f4-add2-bcecee7264f5 · outbound

This paper cites AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment.

RAISE: Realness Assessment for Image Synthesis and Evaluation AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment

Reference 11

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Observation b3a71c26-b085-450a-b77c-f14bc388d0ee · outbound

This paper cites Learning transferable visual models from natural language supervision,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Learning transferable visual models from natural language supervision,

Reference 12

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Observation 8cb0078e-c3a8-40b6-b634-437a46caa98c · outbound

This paper cites Improved techniques for training GANs,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Improved techniques for training GANs,

Reference 13

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Observation b291a419-5e5c-46b7-97a6-ef314a346a68 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local nash equilibrium,.

RAISE: Realness Assessment for Image Synthesis and Evaluation GANs trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 14

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Observation ec75114b-39f3-4db0-af76-4da9d7717a15 · outbound

This paper cites Demystifying MMD GANs.

RAISE: Realness Assessment for Image Synthesis and Evaluation Demystifying MMD GANs

Reference 15

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Observation 2834c795-cd4c-450d-a164-0776b3b10ab0 · outbound

This paper cites NTIRE 2024 quality assessment of ai-generated content challenge,.

RAISE: Realness Assessment for Image Synthesis and Evaluation NTIRE 2024 quality assessment of ai-generated content challenge,

Reference 16

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Observation 4175ae4b-97d5-456e-ba47-8521bd3c5dc5 · outbound

This paper cites Subjective-aligned dataset and metric for text-to-video quality assessment,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Subjective-aligned dataset and metric for text-to-video quality assessment,

Reference 17

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f02f61a9-9447-492e-8f57-b61cf62f44c0 · outbound

This paper cites AiGC image quality assessment via image-prompt correspondence,.

RAISE: Realness Assessment for Image Synthesis and Evaluation AiGC image quality assessment via image-prompt correspondence,

Reference 18

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Observation a5eb529a-8317-4221-bb24-55917e9e2aef · outbound

This paper cites AIGCc-VQA: A holistic perception metric for aigc video quality assessment,.

RAISE: Realness Assessment for Image Synthesis and Evaluation AIGCc-VQA: A holistic perception metric for aigc video quality assessment,

Reference 19

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Observation a2a8f69a-463a-4c3f-afc0-0d3edc34aaff · outbound

This paper cites Global-local image perceptual score (GLIPS): Evaluating photorealistic quality of ai-generated images,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Global-local image perceptual score (GLIPS): Evaluating photorealistic quality of ai-generated images,

Reference 20

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Observation e27610d7-7abe-4cfe-93a1-cd447a89e56f · outbound

This paper cites Seeing is not always believing: Benchmarking human and model perception of ai-generated images,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Seeing is not always believing: Benchmarking human and model perception of ai-generated images,

Reference 21

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 15bc5936-396e-4f4c-98a4-5457bcc07d71 · outbound

This paper cites KADID-10k: A large-scale artificially distorted iqa database,.

RAISE: Realness Assessment for Image Synthesis and Evaluation KADID-10k: A large-scale artificially distorted iqa database,

Reference 22

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Observation 5aee10a8-ace1-437b-954d-233017fac672 · outbound

This paper cites Most apparent distortion: full- reference image quality assessment and the role of strategy,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Most apparent distortion: full- reference image quality assessment and the role of strategy,

Reference 23

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Observation c0191333-fc99-475d-8a2f-a027f3fb5940 · outbound

This paper cites Subjective video quality assessment methods for multimedia applications,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Subjective video quality assessment methods for multimedia applications,

Reference 24

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Observation b2816036-1b62-4480-bd5c-b7af2800d6b7 · outbound

This paper cites Recommendation ITU-R BT.500-15: Methodologies for the subjective assessment of the quality of television images,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Recommendation ITU-R BT.500-15: Methodologies for the subjective assessment of the quality of television images,

Reference 25

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation d2548312-48a4-4b26-b032-c223e07af668 · outbound

This paper cites Exploring varying color spaces through representative forgery learning to improve deepfake detection,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Exploring varying color spaces through representative forgery learning to improve deepfake detection,

Reference 26

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 75d57fdc-457e-4870-8ab6-d2d7027e4fea · outbound

This paper cites Leveraging edges and optical flow on faces for deepfake detection,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Leveraging edges and optical flow on faces for deepfake detection,

Reference 27

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation af757cd9-717e-4bd3-bf54-89bea0a1e78d · outbound

This paper cites DeepFake videos detection based on texture features.

RAISE: Realness Assessment for Image Synthesis and Evaluation DeepFake videos detection based on texture features

Reference 28

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 12e897cf-dcd8-4b2c-9bee-ea5985e329f4 · outbound

This paper cites Textural features for image classification,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Textural features for image classification,

Reference 29

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation a729ec52-5203-47bf-8dfb-cdc962cf8b1e · outbound

This paper cites Histograms of oriented gradients for human detection,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Histograms of oriented gradients for human detection,

Reference 30

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 46f622de-dcff-4ff0-a2fc-f2d7e434656b · outbound

This paper cites Image feature detectors for deepfake video detection,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Image feature detectors for deepfake video detection,

Reference 31

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 2a076abe-d924-4e07-8b5e-c6fbaff569c7 · outbound

This paper cites Leveraging frequency analysis for deep fake image recogni- tion,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Leveraging frequency analysis for deep fake image recogni- tion,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f9d97789-bce7-4359-9e7d-8274d4dd758f · outbound

This paper cites Dropout: a simple way to prevent neural networks from over- fitting,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Dropout: a simple way to prevent neural networks from over- fitting,

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation cdbef4ce-4df8-4176-85bd-645b0d2cb850 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1a53ae7f-ce3d-4b59-978e-80054d60a2a4 · outbound

This paper cites Deep residual learning for image recognition,.

RAISE: Realness Assessment for Image Synthesis and Evaluation Deep residual learning for image recognition,

Reference 35

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Unavailable: canonical work link unavailable.

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Observation b01ca4d0-c894-4a8b-8b87-d6c31a4f7a88 · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

RAISE: Realness Assessment for Image Synthesis and Evaluation ImageNet: A large-scale hierarchical image database,

Reference 36

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raw_fallback, observed 2026-08-07T14:20:22.450823Z

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Pith citing papers

Observation 624949b9-657d-479c-b831-784e40e3ae71 · inbound

Gram-MMD: A Texture-Aware Metric for Image Realism Assessment cites this paper.

Gram-MMD: A Texture-Aware Metric for Image Realism Assessment RAISE: Realness Assessment for Image Synthesis and Evaluation

Reference 5

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arxiv_id, observed 2026-05-13T20:08:12.871694Z

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