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Paper Citation Record · LEDGER

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment

As of 21 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2411.12575.

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

pith.paper-citation-record.v1
2411.12575 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:26:37.719980Z

measured 50 of 50 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 30b41e89-8256-4fd7-8402-b16a7ca1078e · outbound

This paper cites Comparing the robustness of modern no-reference image-and video-quality metrics to adversarial attacks.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Comparing the robustness of modern no-reference image-and video-quality metrics to adversarial attacks

Reference 1

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Observation e1cdf2b3-c367-43f2-a185-214781ba84b1 · outbound

This paper cites Fooling an automatic image quality estimator.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Fooling an automatic image quality estimator

Reference 2

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Observation 9febd3c4-1d7b-42ed-bb81-2b28388e93f6 · outbound

This paper cites (cer- tified!!) adversarial robustness for free! In The Eleventh In- ternational Conference on Learning Representations , 2022.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment (cer- tified!!) adversarial robustness for free! In The Eleventh In- ternational Conference on Learning Representations , 2022

Reference 3

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Observation 26bcb438-0660-462f-8ec7-3c691bca7d4f · outbound

This paper cites Topiq: A top-down approach from semantics to distortions for image quality assessment.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Topiq: A top-down approach from semantics to distortions for image quality assessment

Reference 4

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Observation fe81af8d-3993-47a7-8186-a50bc0c087b7 · outbound

This paper cites Densepure: Understanding diffusion models towards adver- sarial robustness.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Densepure: Understanding diffusion models towards adver- sarial robustness

Reference 5

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Observation dec6fd77-1f45-4084-8fbd-d4a13cce0ee3 · outbound

This paper cites De- tection as regression: Certified object detection with median smoothing.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment De- tection as regression: Certified object detection with median smoothing

Reference 6

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Observation f37f52ae-b86c-4cc3-8ea6-e64a2c161c77 · outbound

This paper cites Increasing the robustness of image quality assessment models through adversarial training.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Increasing the robustness of image quality assessment models through adversarial training

Reference 7

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Observation 031ce39b-f1a3-4131-a38e-197b4b7b570e · outbound

This paper cites Increasing the robustness of image quality assessment models through adversarial training.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Increasing the robustness of image quality assessment models through adversarial training

Reference 8

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Observation eb385355-1a2a-4392-b62b-746260644cc7 · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Certified adversarial robustness via randomized smoothing

Reference 9

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Observation c073bf7f-6890-45dd-a1be-2b9de9db2034 · outbound

This paper cites Sparse adversarial video attack based on dual-branch neural network on industrial artificial intelligence of things.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Sparse adversarial video attack based on dual-branch neural network on industrial artificial intelligence of things

Reference 10

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Observation a5684736-5ff5-490d-8234-ce0ca4ba0444 · outbound

This paper cites Perceptual quality assessment of smartphone photog- raphy.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Perceptual quality assessment of smartphone photog- raphy

Reference 11

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Observation 557f0476-637d-4608-af72-f66e90e4a833 · outbound

This paper cites Massive online crowdsourced study of subjective and objective picture qual- ity.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Massive online crowdsourced study of subjective and objective picture qual- ity

Reference 12

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Observation d29c11ce-b0bd-48f7-a548-0b5fe110ea29 · outbound

This paper cites Lipsim: A provably robust perceptual similarity metric.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Lipsim: A provably robust perceptual similarity metric

Reference 13

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Source-reported events for the cited work

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Observation 590e50ca-73ff-4249-9d04-a136da900a43 · outbound

This paper cites LipSim: A Provably Robust Perceptual Similarity Metric.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment LipSim: A Provably Robust Perceptual Similarity Metric

Reference 14

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Observation 4bfb203c-0ce2-4592-ba16-b7002e4a5f35 · outbound

This paper cites R-LPIPS: An Adversarially Robust Perceptual Similarity Metric.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment R-LPIPS: An Adversarially Robust Perceptual Similarity Metric

Reference 15

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Observation 65497571-eadb-4918-84ee-c50273904f4d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Explaining and Harnessing Adversarial Examples

Reference 16

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Observation f137949c-01df-4ee8-9e7c-d21b72404ba8 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 17

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Observation c2026f4d-01d1-4074-b9e6-c1cba1b8dfec · outbound

This paper cites Guardians of image quality: Benchmark- ing defenses against adversarial attacks on image quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Guardians of image quality: Benchmark- ing defenses against adversarial attacks on image quality metrics

Reference 18

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Observation 8875af73-da7d-458e-a89c-460902e1372d · outbound

This paper cites Adversarial purification for no-reference image-quality metrics: applicability study and new methods.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Adversarial purification for no-reference image-quality metrics: applicability study and new methods

Reference 19

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Observation 96a64c6c-4be6-4cf2-a3db-fc21b958ce74 · outbound

This paper cites Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment

Reference 20

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Observation 49174bae-c9f9-42be-96fc-d09cf40b38ee · outbound

This paper cites Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?

Reference 21

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Observation 09e1ebbb-1521-4875-923e-fb96b30f067e · outbound

This paper cites E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles

Reference 22

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Observation 6dd02337-f530-4c9b-bd00-cf119c34940e · outbound

This paper cites Image robustness to adversarial attacks on no- reference image-quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Image robustness to adversarial attacks on no- reference image-quality metrics

Reference 23

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Observation 4a2714de-9213-40f1-aea1-d693f1992b54 · outbound

This paper cites Adversarial attacks against blind image quality assessment models.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Adversarial attacks against blind image quality assessment models

Reference 24

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Observation c86f2e52-4e45-4d06-80e4-d1e5d31541ab · outbound

This paper cites Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics

Reference 25

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Observation 2498f77a-5154-456c-a7ba-03a11c275030 · outbound

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

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Kadid-10k: A large-scale artificially distorted iqa database

Reference 26

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Observation 2095f92b-7de6-45eb-a0bb-566a04126de9 · outbound

This paper cites Defense against adversarial attacks on no- reference image quality models with gradient norm regu- larization.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Defense against adversarial attacks on no- reference image quality models with gradient norm regu- larization

Reference 27

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Observation 01b1309c-08ad-4d3b-91ef-99749c5ef12f · outbound

This paper cites Evaluating the vulnera- bility of deep learning-based image quality assessment meth- ods to adversarial attacks.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Evaluating the vulnera- bility of deep learning-based image quality assessment meth- ods to adversarial attacks

Reference 28

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Observation e63e217c-c301-41fa-9bf6-964367c6b6dd · outbound

This paper cites Reasons for the superiority of stochastic estima- tors over deterministic ones: Robustness, consistency and perceptual quality.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Reasons for the superiority of stochastic estima- tors over deterministic ones: Robustness, consistency and perceptual quality

Reference 29

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Observation 5d492614-4380-444b-8c20-d4b7b9afad91 · outbound

This paper cites Black-box adversarial attacks against im- age quality assessment models.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Black-box adversarial attacks against im- age quality assessment models

Reference 30

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Observation 1a7168ee-a524-4995-8802-52f7227d6c4f · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment U- net: Convolutional networks for biomedical image segmen- tation

Reference 31

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Observation 4f86be13-b7a0-47b5-9c4d-ca98cda652aa · outbound

This paper cites Denoised smoothing: A provable defense for pretrained classifiers.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Denoised smoothing: A provable defense for pretrained classifiers

Reference 32

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Observation 5bdc0ab1-06a7-4e79-83ea-4a4bbe7b3a70 · outbound

This paper cites Universal perturbation attack on differ- entiable no-reference image- and video-quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Universal perturbation attack on differ- entiable no-reference image- and video-quality metrics

Reference 33

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Observation ed1bfa28-b405-4374-b5f7-a842df9f58e4 · outbound

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Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Unresolved cited work

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 779a55d2-d3a2-4ed0-96ac-61ffcf6247aa · outbound

This paper cites Towards adversarial robustness verification of no- reference image- and video-quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Towards adversarial robustness verification of no- reference image- and video-quality metrics

Reference 35

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation eb490197-8878-4cef-87fd-f61912e0fde0 · outbound

This paper cites an unresolved cited work.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-12T17:26:38.111565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e678564d-93f5-47ad-a82d-4496612bd018 · outbound

This paper cites Applicability limitations of differentiable full-reference image-quality.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Applicability limitations of differentiable full-reference image-quality

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-12T17:26:37.804203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6dd67d2b-8aa1-465b-9a60-1c75e5a49768 · outbound

This paper cites Unveiling the limitations of novel image quality metrics.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Unveiling the limitations of novel image quality metrics

Reference 38

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fa4524fe-5e11-442d-91ec-c5a34c92f9c4 · outbound

This paper cites Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0a5a7257-973f-4179-908b-2eccb50359f2 · outbound

This paper cites Mem- net: A persistent memory network for image restoration.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Mem- net: A persistent memory network for image restoration

Reference 40

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.681443Z digest=sha256:c555881efdace709a82ec18876d57f317b4d1f9f0b43eaefbfbb7631c66cd1ad

Observation 9bed6eed-1718-42fd-bd59-60a69d3d98ef · outbound

This paper cites Ex- ploring clip for assessing the look and feel of images.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Ex- ploring clip for assessing the look and feel of images

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:38.061919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.685438Z digest=sha256:8d82ba37c86e9dae651b5794224cd77312f5a426c8b312d42fd115f86769a324

Observation 7729ccad-47e8-4182-be82-5801af1bf9e6 · outbound

This paper cites Exploring vulnerabilities of no-reference image quality as- sessment models: A query-based black-box method.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Exploring vulnerabilities of no-reference image quality as- sessment models: A query-based black-box method

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:38.049147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.689254Z digest=sha256:dcdd77be1b3eed38635baddace896d2133fc172336d6591f63cf7e0383abd091

Observation 29e414df-d344-47e2-a223-b4e759c5662c · outbound

This paper cites Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T17:26:37.692890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:26:37.692890Z digest=sha256:350040280c06b784dad72cbdbb479827f7785c27eb810a510b2da8d5985e2a3b

Observation 2cedfed0-9517-4d8f-9bb4-e602dd75aaa9 · outbound

This paper cites Vulnerabilities in video quality assessment models: The challenge of adversarial attacks.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Vulnerabilities in video quality assessment models: The challenge of adversarial attacks

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:38.035227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.697074Z digest=sha256:da6c0f80234866cac54e74b8dd2457181f85e4982ab913bd0dafb90963d9bdc4

Observation b7fec684-0f6d-42b9-ac1b-8925c85f05fc · outbound

This paper cites Secure Video Quality Assessment Resisting Adversarial Attacks.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Secure Video Quality Assessment Resisting Adversarial Attacks

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-12T17:26:37.776926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.701561Z digest=sha256:c02bdbd1ca754babc78ab900858e2f2f251ca1c78d05f268d68277b6717e2bf8

Observation 99d9b981-cf68-4c63-a6de-f5e81ec164d4 · outbound

This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:38.021960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.705231Z digest=sha256:ea7b8bf8a6702f6b2199c986b5ca219d4c8b264675ed80d9481cb70ec415f669

Observation c6df1403-394d-4d0b-a408-f4ca74964bdd · outbound

This paper cites Blind image quality assessment using a deep bilinear convolutional neural network.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Blind image quality assessment using a deep bilinear convolutional neural network

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:38.009408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.708658Z digest=sha256:4c8951287921a126e2daf7e3bb223f7d64e4e49e1c0231f8a80bd65e1a2c8192

Observation 101d190e-60fe-4c77-82b7-d10248913414 · outbound

This paper cites Perceptual at- tacks of no-reference image quality models with human-in- the-loop.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Perceptual at- tacks of no-reference image quality models with human-in- the-loop

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:37.996870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.712128Z digest=sha256:91dfc8a6d2d1aefd46e74bfa776bb61a8ebdcf4e167482a91eed3a6d49323d86

Observation 48cbb1f6-be55-4b4e-a4f8-1670b40094bd · outbound

This paper cites Convolutional neural networks for image denoising and restoration.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Convolutional neural networks for image denoising and restoration

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:26:37.983911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-12T17:26:37.716192Z digest=sha256:781a60227dda1cb59809779ffe2950129fd8c4a75b980978eca296cd745e1809

Observation 72d5bc0e-9264-422b-a022-54707052d846 · outbound

This paper cites Hacking VMAF with Video Color and Contrast Distortion.

Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment Hacking VMAF with Video Color and Contrast Distortion

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-12T17:26:37.760563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.