Paper Citation Record · LEDGER

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2411.11795.

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

pith.paper-citation-record.v1
2411.11795 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:10:08.838524Z

measured 45 of 45 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

45 of 45 outbound references displayed

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verified fuzzy39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c94f2c3d-f802-48ae-8e1e-eb6a12b2c201 · outbound

This paper cites Soft-to-hard vector quantization for end-to-end learn- ing compressible representations.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Soft-to-hard vector quantization for end-to-end learn- ing compressible representations

Reference 1

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Observation 9e285832-e098-493f-a665-b585a1637555 · outbound

This paper cites Video compression dataset and benchmark of learning-based video-quality metrics.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Video compression dataset and benchmark of learning-based video-quality metrics

Reference 2

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Observation 1fe8e5be-46aa-4b4b-904b-818bdc2177b1 · outbound

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

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Comparing the robustness of modern no-reference image- and video-quality metrics to adversarial attacks

Reference 3

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Observation 18b8d4c6-712d-411f-b987-964e7d386a59 · outbound

This paper cites Contour detection and hierarchical image seg- mentation.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Contour detection and hierarchical image seg- mentation

Reference 4

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Observation 2292dd63-e972-4d6c-898b-7fd0105fd91e · outbound

This paper cites The jpeg ai standard: Providing efficient human and machine vi- sual data consumption.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods The jpeg ai standard: Providing efficient human and machine vi- sual data consumption

Reference 5

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Observation da88b0c9-f84a-4c21-a8e1-0b77dd36a69a · outbound

This paper cites End-to-end Optimized Image Compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods End-to-end Optimized Image Compression

Reference 6

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Observation f4cc8404-b110-4baa-a98d-3a3ab958e52b · outbound

This paper cites Variational image compres- sion with a scale hyperprior.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Variational image compres- sion with a scale hyperprior

Reference 7

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Observation 2cc57b0a-bbd4-4909-84c2-fb1d273ada02 · outbound

This paper cites Unrestricted Adversarial Examples via Semantic Manipulation.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Unrestricted Adversarial Examples via Semantic Manipulation

Reference 8

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Observation 6b5c035d-19aa-4628-a71f-5838c05bc00e · outbound

This paper cites Towards evaluating the robustness of neural networks.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Towards evaluating the robustness of neural networks

Reference 9

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Observation 3292f4f5-ab02-48ed-8b74-e827ce95649f · outbound

This paper cites A survey on adversarial attacks and defences.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods A survey on adversarial attacks and defences

Reference 10

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Observation e55a42bb-c433-43d7-b862-eb391db6485c · outbound

This paper cites Toward robust neural image com- pression: Adversarial attack and model finetuning.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Toward robust neural image com- pression: Adversarial attack and model finetuning

Reference 11

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Observation 2c0d817c-0dd0-4ed0-be19-cc1c46fb94df · outbound

This paper cites Toward robust neural image com- pression: Adversarial attack and model finetuning.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Toward robust neural image com- pression: Adversarial attack and model finetuning

Reference 12

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Observation a45a66c1-0fee-4103-8116-549f4fa66baf · outbound

This paper cites Learned image compression with discretized gaussian mixture likelihoods and attention modules.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Learned image compression with discretized gaussian mixture likelihoods and attention modules

Reference 13

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Observation 8ff8fdff-475d-4668-bf96-28dbb45ceec3 · outbound

This paper cites Nips 2017: Adversarial learning devel- opment set.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Nips 2017: Adversarial learning devel- opment set

Reference 14

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Observation 634d5fac-0f5b-4d8a-a6ef-dde37e606a4f · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods The cityscapes dataset for semantic urban scene understanding

Reference 15

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Observation a098237b-2f84-4b94-9af4-4bde0aab123f · outbound

This paper cites Boosting adversarial at- tacks with momentum.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Boosting adversarial at- tacks with momentum

Reference 16

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Observation 2bc99580-39fa-476e-bcaf-0194cc3676c9 · outbound

This paper cites Lossy image compression with quantized hierarchical vaes.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Lossy image compression with quantized hierarchical vaes

Reference 17

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Observation 762f0954-17ca-45bd-a10e-515b75d19a42 · outbound

This paper cites Neural image com- pression via attentional multi-scale back projection and fre- quency decomposition.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Neural image com- pression via attentional multi-scale back projection and fre- quency decomposition

Reference 18

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This paper cites Elic: Efficient learned image compres- sion with unevenly grouped space-channel contextual adap- tive coding.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Elic: Efficient learned image compres- sion with unevenly grouped space-channel contextual adap- tive coding

Reference 19

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Observation d564f530-2555-4ed8-8ad9-299922deb34a · outbound

This paper cites Kodak lossless true color image suite.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Kodak lossless true color image suite

Reference 20

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Observation 2459db9c-3550-4d59-b06d-f67081f8a661 · outbound

This paper cites Goodfellow, and Samy Bengio.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Goodfellow, and Samy Bengio

Reference 21

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Observation d730c84f-defe-4c17-a0ed-f0300bc405b8 · outbound

This paper cites Deep contextual video com- pression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Deep contextual video com- pression

Reference 22

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Observation f00bd9a0-de82-46c2-9601-f78af3bb1046 · outbound

This paper cites Vmaf: The journey continues.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Vmaf: The journey continues

Reference 23

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Observation 6f48a617-5ffc-4961-82d6-2614c9610419 · outbound

This paper cites Manipulation attacks on learned image com- pression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Manipulation attacks on learned image com- pression

Reference 24

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Observation 70e6d93c-5f15-4669-9b03-0da09cc552ff · outbound

This paper cites Learned image compression with mixed transformer-cnn architectures.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Learned image compression with mixed transformer-cnn architectures

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:09.060761Z

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Observation 6b785482-4ff6-4634-b38c-8e73f1bf3b31 · outbound

This paper cites Frequency-driven imperceptible ad- versarial attack on semantic similarity.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Frequency-driven imperceptible ad- versarial attack on semantic similarity

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:09.052390Z

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Observation aeb9da19-541f-4fb0-a489-18e7675f10dd · outbound

This paper cites Towards deep learn- ing models resistant to adversarial attacks.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Towards deep learn- ing models resistant to adversarial attacks

Reference 27

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Observation 26200545-068a-4ad3-9acf-a6e743830339 · outbound

This paper cites High-fidelity generative image compres- sion.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods High-fidelity generative image compres- sion

Reference 28

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Observation e45e3221-021e-4b7f-a04c-83f4d40846b2 · outbound

This paper cites Joint autoregressive and hierarchical priors for learned im- age compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Joint autoregressive and hierarchical priors for learned im- age compression

Reference 29

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Observation 50efed2f-c974-4fc8-9b3a-0800252be804 · outbound

This paper cites Pick-object-attack: Type-specific adver- sarial attack for object detection.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Pick-object-attack: Type-specific adver- sarial attack for object detection

Reference 30

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Observation cd20691b-e38c-4dcc-9167-25187d306e8d · outbound

This paper cites Diffusion Models for Adversarial Purification.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Diffusion Models for Adversarial Purification

Reference 31

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This paper cites The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations

Reference 32

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Observation eb884a24-916f-4bd9-83a3-43872f15f662 · outbound

This paper cites Jpeg ai image compression visual artifacts: Detection methods and dataset, 2024.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Jpeg ai image compression visual artifacts: Detection methods and dataset, 2024

Reference 33

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Observation 49b5c643-0832-4195-a4e1-f53711b2182f · outbound

This paper cites EVC: Towards Real-Time Neural Image Compression with Mask Decay.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods EVC: Towards Real-Time Neural Image Compression with Mask Decay

Reference 34

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Observation 647bf31f-76bd-4608-a29f-707b45ad254f · outbound

This paper cites Simoncelli.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Simoncelli

Reference 35

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Observation c41418de-0eff-4298-8ed2-742728bf25f2 · outbound

This paper cites Wang, E.P.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Wang, E.P

Reference 36

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Observation f3172bc8-ea3c-4bdb-9785-7156b3f782f8 · outbound

This paper cites Physical adversarial attack meets computer vision: A decade survey.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Physical adversarial attack meets computer vision: A decade survey

Reference 37

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Observation 34b2b2a4-9d09-4e41-bd1a-92bd54a3268d · outbound

This paper cites Slimmable compressive autoencoders for practical neural image compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Slimmable compressive autoencoders for practical neural image compression

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.963398Z

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Observation 169c5ac1-3957-45d4-aaa9-7479ae8ec846 · outbound

This paper cites Slimmable compressive autoencoders for practical neural image compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Slimmable compressive autoencoders for practical neural image compression

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.953119Z

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Observation e0e0250c-cd10-4fdf-9b50-a94f85c4d0e4 · outbound

This paper cites Lossy image compression with conditional diffusion models.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Lossy image compression with conditional diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.943925Z

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Observation cf014336-bd7f-406e-8be7-0e0aae9145aa · outbound

This paper cites Improving inference for neural image compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Improving inference for neural image compression

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.934781Z

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Observation 68bf4bc4-0c19-481f-9d19-4a82ca7915ad · outbound

This paper cites A Survey On Universal Adversarial Attack.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods A Survey On Universal Adversarial Attack

Reference 42

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Observation daff2c3e-e953-46c7-b3f8-8a3c2c2a042f · outbound

This paper cites Attack and Defense Analysis of Learned Image Compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Attack and Defense Analysis of Learned Image Compression

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:10:08.865669Z

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Observation 677e9f9b-1847-4772-a010-e1ecb7897999 · outbound

This paper cites The devil is in the details: Window-based attention for image compression.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods The devil is in the details: Window-based attention for image compression

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.925293Z

Source-reported events for the cited work

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Observation e78e51b7-8210-4abf-8c72-5cf5cd247f80 · outbound

This paper cites Bsq-rate: a new approach for video-codec performance comparison and drawbacks of current solutions.

Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods Bsq-rate: a new approach for video-codec performance comparison and drawbacks of current solutions

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:10:08.914828Z

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.

Pith citing papers

No inbound Pith citation observations are available.