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Paper Citation Record · LEDGER

UVCG: Leveraging Temporal Consistency for Universal Video Protection

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2411.17746.

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

pith.paper-citation-record.v1
2411.17746 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:30:24.636180Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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  • verified fuzzy17
  • unresolved19
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 54eb0ef6-9e67-4662-ac09-7c1acacfe065 · outbound

This paper cites iedit: Lo- calised text-guided image editing with weak supervision.

UVCG: Leveraging Temporal Consistency for Universal Video Protection iedit: Lo- calised text-guided image editing with weak supervision

Reference 1

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 3e6edb32-ee22-46dc-8f18-21c960620e4e · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

UVCG: Leveraging Temporal Consistency for Universal Video Protection In- structpix2pix: Learning to follow image editing instructions

Reference 2

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Observation 8632fa81-9a77-4672-9f0f-7ad83c42e723 · outbound

This paper cites In- structpix2pix: Learning to follow image editing instructions.

UVCG: Leveraging Temporal Consistency for Universal Video Protection In- structpix2pix: Learning to follow image editing instructions

Reference 3

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation dad12d61-2743-451d-9306-4cae2c5c760c · outbound

This paper cites Structure 8 and content-guided video synthesis with diffusion models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Structure 8 and content-guided video synthesis with diffusion models

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.256673Z digest=sha256:9e908f581ade607da3212245738492377f154777f36f2bd22d955522ef653363

Observation 52748955-f8e5-4790-8cb6-407133f113e8 · outbound

This paper cites TokenFlow: Consistent Diffusion Features for Consistent Video Editing.

UVCG: Leveraging Temporal Consistency for Universal Video Protection TokenFlow: Consistent Diffusion Features for Consistent Video Editing

Reference 5

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source=pdf_text observed=2026-08-12T13:30:24.266942Z digest=sha256:5507930325dc91eb17562f876f8a2282a0f3439454298a8718003c1805c36983

Observation b31b2dac-a7fd-4cc4-8332-0e3e375da043 · outbound

This paper cites Generative adversarial nets.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Generative adversarial nets

Reference 6

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source=pdf_text observed=2026-08-12T13:30:24.285105Z digest=sha256:ea1dd31df8c0223efa675afb9b9e4907b02f9405fdc01551cd4dd2ec108ab200

Observation 8335c92c-c942-4417-88f7-834277ccd817 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Explaining and Harnessing Adversarial Examples

Reference 7

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source=pdf_text observed=2026-08-12T13:30:24.293841Z digest=sha256:929b1e806549301c043badffdcb69bafd4255a60eab03396bc2b5c986cd3c612

Observation 2cf2c84c-a42e-4f52-90cb-243bb14817f2 · outbound

This paper cites A Survey on Responsible Generative AI: What to Generate and What Not.

UVCG: Leveraging Temporal Consistency for Universal Video Protection A Survey on Responsible Generative AI: What to Generate and What Not

Reference 8

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source=pdf_text observed=2026-08-12T13:30:24.299778Z digest=sha256:23a7c549ca36dd50da4b09c7bc1bb6df33aeff8633bb576d11369dce63575c62

Observation 986f5140-7455-4e3c-96b3-b0808b260783 · outbound

This paper cites Diff-privacy: Diffusion-based face privacy pro- tection.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Diff-privacy: Diffusion-based face privacy pro- tection

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.313096Z digest=sha256:bcf417c135f7cc91e4f28b1fc8ce4d04c3f79e2095ddcc5c4b3d6fb5e62ee623

Observation 325c807a-6768-42c1-9b75-586711178cc7 · outbound

This paper cites Denoising dif- fusion probabilistic models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Denoising dif- fusion probabilistic models

Reference 10

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source=pdf_text observed=2026-08-12T13:30:24.324099Z digest=sha256:c8c53f7d9b843b2b0176a625052b0dbc628d2d0315182119d2ec9d172e8b807a

Observation ede97725-de2a-495e-8756-c3eeca17fda6 · outbound

This paper cites Diffattack: Eva- sion attacks against diffusion-based adversarial purification.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Diffattack: Eva- sion attacks against diffusion-based adversarial purification

Reference 11

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.329570Z digest=sha256:6330e20e5224c7e4a817f6ebacbcfa546f352dda70950558cc12e862850c710f

Observation 2c2395d9-c6ee-4099-8c8b-89d04b59b171 · outbound

This paper cites Text2video-zero: Text- to-image diffusion models are zero-shot video generators.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Text2video-zero: Text- to-image diffusion models are zero-shot video generators

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.335664Z digest=sha256:5d73a9ca1e11e07183e696464eaae35ba640d2e878e599ae44c65543c84d63ee

Observation 21b8b52e-54ff-49fd-acd1-5c7d5508f6a4 · outbound

This paper cites Auto-Encoding Variational Bayes.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Auto-Encoding Variational Bayes

Reference 13

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source=pdf_text observed=2026-08-12T13:30:24.344609Z digest=sha256:b40d41b0aea0f4b4c86766d73de7f1a9cad479ca1153f4a2783c189cb08bd8d0

Observation e5610bb4-d23b-4a3c-a422-8d3bbaae414b · outbound

This paper cites PRIME: Protect Your Videos From Malicious Editing.

UVCG: Leveraging Temporal Consistency for Universal Video Protection PRIME: Protect Your Videos From Malicious Editing

Reference 14

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source=pdf_text observed=2026-08-12T13:30:24.351747Z digest=sha256:caa948dc4a1e4ff51c40e5d145c09983964397043d056b3edb9fadb5863f42c5

Observation 791e28a8-f5c8-4485-be19-680ea229ec5b · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.371411Z digest=sha256:6aeeef9e01fd26bee65a0c012ddad74648950bd28f73b37b9e7bcbae0b806845

Observation 1f544ff6-1f31-4a53-8710-3b07758b7595 · outbound

This paper cites Mist: Towards Improved Adversarial Examples for Diffusion Models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Mist: Towards Improved Adversarial Examples for Diffusion Models

Reference 16

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source=pdf_text observed=2026-08-12T13:30:24.379682Z digest=sha256:46f432d6f6f8c82cbefe0698c27c78ff299beee1f1f19992d9be06266b3d6d7e

Observation 63e5e468-6505-43be-a730-50b79a1265e8 · outbound

This paper cites Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Reference 17

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source=pdf_text observed=2026-08-12T13:30:24.394470Z digest=sha256:0c10d1e5273164df3e9b994ba0c06aaffaddd6010e3315c446202e106aa3a477

Observation 1331800f-f885-4b43-9aa5-e93788fc38de · outbound

This paper cites Inter-frame Accelerate Attack against Video Interpolation Models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Inter-frame Accelerate Attack against Video Interpolation Models

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 545add3d-a145-4232-9a6c-f1b174af4007 · outbound

This paper cites Video-p2p: Video editing with cross-attention control.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Video-p2p: Video editing with cross-attention control

Reference 19

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source=pdf_text observed=2026-08-12T13:30:24.413385Z digest=sha256:5acb67ff98ceed72cc13b4a5cf0c7ac373b02ebba918e60babf109ef40e62798

Observation 08903cef-eb2b-4adf-902d-7877c10c8c0a · outbound

This paper cites Delving into Transferable Adversarial Examples and Black-box Attacks.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Delving into Transferable Adversarial Examples and Black-box Attacks

Reference 20

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source=pdf_text observed=2026-08-12T13:30:24.418142Z digest=sha256:f483ba117cd2ae790f9c5809deef14da23af55bb70a7e6f63e37df9283de59fc

Observation f75808f1-d4a5-4cab-8c93-ae8e57e16bb1 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

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source=pdf_text observed=2026-08-12T13:30:24.425318Z digest=sha256:9cbb67f8d6371385ae9667e5143669aafee614e067a3cb40cbd72fc3dd480220

Observation 171001c7-8294-4fa4-9007-81222bab0fac · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 22

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source=pdf_text observed=2026-08-12T13:30:24.432778Z digest=sha256:8241cee20f579673976a5c2cba0c3910960929f735e4f17f5a98105fb365adae

Observation 5b90865e-7417-4698-8b7c-3104254b9330 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

UVCG: Leveraging Temporal Consistency for Universal Video Protection The 2017 DAVIS Challenge on Video Object Segmentation

Reference 23

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source=pdf_text observed=2026-08-12T13:30:24.442827Z digest=sha256:f9949fba538063d3bd9c66edd2bc5669d80c275be8ccf2f0c37821f8a183ba9e

Observation 2888a429-a9df-4ad7-bff3-fc18a67b46e2 · outbound

This paper cites Fatezero: Fus- ing attentions for zero-shot text-based video editing.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Fatezero: Fus- ing attentions for zero-shot text-based video editing

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.454161Z digest=sha256:9592664f2219d67c4e3aa94934c804292918275d3ab222bd6aaf833db6be5e33

Observation 54ae7841-1e6f-4b09-9d22-d998b5d5636f · outbound

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

UVCG: Leveraging Temporal Consistency for Universal Video Protection Learning transferable visual models from natural language supervi- sion

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 7038d4d7-d7b2-461c-b332-d84da9a8ee31 · outbound

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

UVCG: Leveraging Temporal Consistency for Universal Video Protection High-resolution image syn- thesis with latent diffusion models

Reference 26

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source=pdf_text observed=2026-08-12T13:30:24.485246Z digest=sha256:8c855320f7954ee54efeb2122608d15a74f1e73eaf0baf0fe1173d3599327265

Observation 983d9055-8361-420a-9cf4-333f39cb1580 · outbound

This paper cites Raising the Cost of Malicious AI-Powered Image Editing.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Raising the Cost of Malicious AI-Powered Image Editing

Reference 27

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source=pdf_text observed=2026-08-12T13:30:24.498279Z digest=sha256:ac41eded8a323fd09dad7fb66278f03d0b679f00aefd4042f5eb8329f10db839

Observation 35b2fb38-8d6c-4e7b-95a6-560d210710da · outbound

This paper cites Glaze: Protecting artists from style mimicry by{Text-to-Image} models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Glaze: Protecting artists from style mimicry by{Text-to-Image} models

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 635c7bf5-83ae-4a32-9a1d-7b2e525951f0 · outbound

This paper cites Image information and visual quality.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Image information and visual quality

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.515742Z digest=sha256:ff69254f25a41c401c60772007cb3ed084fd32d5ed0d1f670d8140ab193fb36f

Observation b51c9ffe-067c-4660-8bba-139edcbec1d4 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Image quality assessment: from error visibility to structural similarity

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.523551Z digest=sha256:07ef71286517732d1cbe368e8f6066c172ac9b1c8438f360c7689301e918ee8b

Observation 597d66c7-8730-4ba6-ba28-106a1f1528dc · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.531607Z digest=sha256:219746fe91df28d6f324c6abdb252a9ccd84bc6962e8d4db74bbdc4ea4656e21

Observation 564cae59-91df-4544-95dd-115c3e713994 · outbound

This paper cites Cross: Diffusion model makes controllable, robust and se- cure image steganography.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Cross: Diffusion model makes controllable, robust and se- cure image steganography

Reference 32

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raw_fallback, observed 2026-08-12T13:30:25.585944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.551806Z digest=sha256:39c4295ed33e92fc2568fbfd4396a54c11e84e92e858bdfc5358326c65f606c6

Observation 2a4d7e6f-9ae3-4cf6-a978-e5a8a4178000 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

UVCG: Leveraging Temporal Consistency for Universal Video Protection The unreasonable effectiveness of deep features as a perceptual metric

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c110c7e5-4918-4660-bc61-f98bbed5fc88 · outbound

This paper cites Sine: Single image editing with text-to-image diffusion models.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Sine: Single image editing with text-to-image diffusion models

Reference 34

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raw_fallback, observed 2026-08-12T13:30:25.561006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.583958Z digest=sha256:20248999bb8c4d986e74e259bb82f244e936e4c0f8e94a057ae726b027789f40

Observation 5dd429da-ec93-4c47-8eab-f7c46758a06c · outbound

This paper cites Understanding and improving adversarial attacks on latent diffusion model.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Understanding and improving adversarial attacks on latent diffusion model

Reference 35

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:30:24.604166Z digest=sha256:6018a23eb57d9bfd37ed43b09c40efe3d4d3f4091a063609f5f355f5cae171e3

Observation 66641ced-0804-4018-aaa2-7c660b00133e · outbound

This paper cites an unresolved cited work.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Unresolved cited work

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.618187Z digest=sha256:1697e971e5c5cac3594ba462ac7a202c172714ab47154f071defac5511e23371

Observation f1611bc8-c6b8-4e64-9563-ed0261ea640f · outbound

This paper cites Figure 9, figure 10 and figure 11 showcase video protection results using our method on Fatezero.

UVCG: Leveraging Temporal Consistency for Universal Video Protection Figure 9, figure 10 and figure 11 showcase video protection results using our method on Fatezero

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T13:30:25.497767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T13:30:24.636180Z digest=sha256:ec8dda0627bac46799109d5ebb2ff071aee1fdeb6a0ab49a3315ebabf7a1d7fb

Pith citing papers

No inbound Pith citation observations are available.