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Paper Citation Record · LEDGER

Observation-Level Watermarking and Detection for Tabular Data

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.10554.

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

pith.paper-citation-record.v1
2607.10554 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:52:33.767274Z

measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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Outbound references

Observation 9aade80a-01b3-41f8-b9b6-1fe94dfd8f69 · outbound

This paper cites Watermarking of large language models.

Observation-Level Watermarking and Detection for Tabular Data Watermarking of large language models

Reference 1

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:f84ad65a83d4e1a30c31e64732cff0a3830cc79ce17381481227fe53c81267a2

Observation c4fa5471-1591-44ba-bfc6-69a8c48cf498 · outbound

This paper cites Optimal spread spectrum watermark embedding via a multistep feasibility formulation.IEEE Transactions on Image Processing, 18(2):371–387, 2009.

Observation-Level Watermarking and Detection for Tabular Data Optimal spread spectrum watermark embedding via a multistep feasibility formulation.IEEE Transactions on Image Processing, 18(2):371–387, 2009

Reference 2

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:ffec13ec193f7cc5aa80d0e1ee26190a004e8f4c630189972cdcae266925c33d

Observation 08c4cbee-cdbb-4871-9308-c7ced065be7b · outbound

This paper cites Natural language watermarking: Design, analysis, and a proof-of-concept implementation.

Observation-Level Watermarking and Detection for Tabular Data Natural language watermarking: Design, analysis, and a proof-of-concept implementation

Reference 3

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:84f0cfc8719ece9e136942efa7543cd3e7dafaed67abb4688c05eaadc2c30c6c

Observation 630b4d1d-e1a8-4cef-90b3-facede195bf4 · outbound

This paper cites Distribution-invariant differential privacy.Journal of Economet- rics, 235(2):444–453, 2023.

Observation-Level Watermarking and Detection for Tabular Data Distribution-invariant differential privacy.Journal of Economet- rics, 235(2):444–453, 2023

Reference 4

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:ff4c4be313ce668f7918d247189c66e8da34d89125ad743f93a6ba15f0cda72e

Observation ef623f12-dff3-45c3-a594-fcdc520db610 · outbound

This paper cites Differential privacy.

Observation-Level Watermarking and Detection for Tabular Data Differential privacy

Reference 5

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:f64a566f8c28ebfc1706b0b996233e755cf9854a048d24a5b200335ef04bc2e6

Observation 7110302c-d3ed-4bce-b718-9b3800434af4 · outbound

This paper cites MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection.

Observation-Level Watermarking and Detection for Tabular Data MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection

Reference 6

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:535966c55fae5747c1336d09aae77250dd16422e575d6df22fa4fdeb289d88b6

Observation e719082c-ea4a-461a-a6f8-a6ea96c7325f · outbound

This paper cites An undetectable watermark for generative image models.

Observation-Level Watermarking and Detection for Tabular Data An undetectable watermark for generative image models

Reference 7

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:5df85e7cddbe55c2d449206244d9d17df5a458928afe6d3d6982b332c86b2eb7

Observation 2f87c51a-2793-4145-823a-a1a027329b08 · outbound

This paper cites Watermarking Generative Tabular Data.

Observation-Level Watermarking and Detection for Tabular Data Watermarking Generative Tabular Data

Reference 8

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:114e36d7d0348c5d29de66be626a7dfa2b78ab619500a1dc286e854e357c5fa5

Observation 33533166-c129-4fe8-9c05-1b1f93a311c6 · outbound

This paper cites Algorithmically effective differentially private synthetic data.

Observation-Level Watermarking and Detection for Tabular Data Algorithmically effective differentially private synthetic data

Reference 9

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:3d0da9d97ae3d51d350a48cabdafa15aecdc8b4a64c94ca04e404ac54ebb056f

Observation 300a9e11-d182-4bab-903c-f9f92dcdc1cd · outbound

This paper cites Dct-domain watermarking techniques for still images: Detector performance analysis and a new structure.IEEE Transactions on Image Processing, 9(1):55–68, 2000.

Observation-Level Watermarking and Detection for Tabular Data Dct-domain watermarking techniques for still images: Detector performance analysis and a new structure.IEEE Transactions on Image Processing, 9(1):55–68, 2000

Reference 10

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:24d8ecac0df74e0e99074277f7e7a4732b1cd2498f8aeafb90ade43d30f3d95e

Observation 6a76202a-f609-49e7-939f-bd89650dd7dc · outbound

This paper cites Unbiased watermark for large language models.

Observation-Level Watermarking and Detection for Tabular Data Unbiased watermark for large language models

Reference 11

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:5cf7a5950955fa1f28bd7a64b4d7d063a838aa9a395d5b1fad95925ae416b8fa

Observation 0ef06c87-8aa1-4c08-8919-efb58e1bbdf2 · outbound

This paper cites A watermark for large language models.

Observation-Level Watermarking and Detection for Tabular Data A watermark for large language models

Reference 12

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:f5e9fa5c6a6523aab58a752168e9a7827fd4f5ff94c7601a3413d56fa86b819b

Observation b6998b2e-c56f-4622-a770-6eb496121d78 · outbound

This paper cites A statistical framework of watermarks for large language models: Pivot, detection efficiency and optimal rules.The Annals of Statistics, 53(1):322–351, 2025.

Observation-Level Watermarking and Detection for Tabular Data A statistical framework of watermarks for large language models: Pivot, detection efficiency and optimal rules.The Annals of Statistics, 53(1):322–351, 2025

Reference 13

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:e867cb7518064ad2194103bea5db9894c8cfceb932fe747c7d1736d7c6949ccd

Observation b6a05f25-bee6-41e5-a025-54349fd900f8 · outbound

This paper cites Wasa: Watermark-based source attribution for large language model-generated data.

Observation-Level Watermarking and Detection for Tabular Data Wasa: Watermark-based source attribution for large language model-generated data

Reference 14

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:b8e6a1f11ef4b96df662b0cdbd3f8996851d823d48e37f5d82fee2326b1c78a5

Observation 5daf3f63-decf-492b-999e-b37ab64f7673 · outbound

This paper cites A data-driven approach to predict the success of bank telemarketing.Decision Support Systems, 62:22–31, 2014.

Observation-Level Watermarking and Detection for Tabular Data A data-driven approach to predict the success of bank telemarketing.Decision Support Systems, 62:22–31, 2014

Reference 15

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:bb7a92b1028ddc4b8784c76f6191e14ab6b052af8853e776ac1711566cd2d0b5

Observation a17d7349-780f-47f2-910a-e505d1fe5ba4 · outbound

This paper cites Black-box forgery attacks on semantic watermarks for diffusion models.

Observation-Level Watermarking and Detection for Tabular Data Black-box forgery attacks on semantic watermarks for diffusion models

Reference 16

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:471184b35b384cbd3c4b375293c673c6c2057abd9293e7d46ca9baa0530e1f91

Observation d810cb9a-3497-48fa-9127-f2f177c5ddb1 · outbound

This paper cites National Health and Nutrition Health Survey 2013-2014 (NHANES) Age Prediction Subset.

Observation-Level Watermarking and Detection for Tabular Data National Health and Nutrition Health Survey 2013-2014 (NHANES) Age Prediction Subset

Reference 17

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doi, observed 2026-07-14T11:00:24.743415Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:a2063041b787fc29fff4c5964e085e15be04a559fa777a5db120001a4b69e940

Observation 41ee3b31-0088-4360-9fdd-927d8872c105 · outbound

This paper cites Adaptive and Robust Watermark for Generative Tabular Data.

Observation-Level Watermarking and Detection for Tabular Data Adaptive and Robust Watermark for Generative Tabular Data

Reference 18

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:a61f5601da93c51d69aafdb870aa85b1800985194f2000c956855cd080f14f02

Observation 14e3cc34-9a68-4a8d-bbef-5754cfba78e7 · outbound

This paper cites Unispach: A text-based data hiding method using unicode space characters.Journal of Systems and Software, 85(5):1075–1082, 2012.

Observation-Level Watermarking and Detection for Tabular Data Unispach: A text-based data hiding method using unicode space characters.Journal of Systems and Software, 85(5):1075–1082, 2012

Reference 19

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:c05322575b260e90b6945051df4a1aab62279b68a01239c71a80509ef4c02389

Observation c88a5679-1691-40ef-a476-8a4a75782b8f · outbound

This paper cites Stegastamp: Invisible hyperlinks in physical photographs.

Observation-Level Watermarking and Detection for Tabular Data Stegastamp: Invisible hyperlinks in physical photographs

Reference 20

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:4cf01253c08228333cc871ed3860138885fdd21a1a92ff54353f838fc0cce48e

Observation 8cff93d1-58f0-4b40-8984-c4932aaac6a1 · outbound

This paper cites Perceptive self-supervised learning network for noisy image watermark removal.IEEE Transactions on Circuits and Systems for Video Technology, 34(8):7069–7079, 2024.

Observation-Level Watermarking and Detection for Tabular Data Perceptive self-supervised learning network for noisy image watermark removal.IEEE Transactions on Circuits and Systems for Video Technology, 34(8):7069–7079, 2024

Reference 21

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:23ce835435b93aa58bf29b46ff427bf1b543372e4055d05d119946062e61122a

Observation 9ce38d57-efff-4cc7-8485-5b5aea1996f9 · outbound

This paper cites Robust blind image watermarking based on interest points.Virtual Reality & Intelligent Hardware, 6(4):308–322, 2024.

Observation-Level Watermarking and Detection for Tabular Data Robust blind image watermarking based on interest points.Virtual Reality & Intelligent Hardware, 6(4):308–322, 2024

Reference 22

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:5ea1074e659ccfff76b0a29d8040a3dbd3c0ee4bc19e4edd6b61d659cf59a2fa

Observation 90e92210-8a40-4088-9652-ac4f0a237d49 · outbound

This paper cites Raw: A robust and agile plug-and-play watermark framework for ai-generated images with provable guarantees.Advances in Neural Information Processing Systems, 37:132077–132105, 2024.

Observation-Level Watermarking and Detection for Tabular Data Raw: A robust and agile plug-and-play watermark framework for ai-generated images with provable guarantees.Advances in Neural Information Processing Systems, 37:132077–132105, 2024

Reference 23

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:665983683c739f1257bbd8672755c1b25d63c6d245a1040a8c3e24fb7a9d82c7

Observation 4b875afe-3632-4858-bed0-e5218c13c336 · outbound

This paper cites Watermarking Text Generated by Black-Box Language Models.

Observation-Level Watermarking and Detection for Tabular Data Watermarking Text Generated by Black-Box Language Models

Reference 24

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:84c86aa74190d5aa01b049d68595753fb8a96fe6b8a01d6249c9fb1045b201d2

Observation 69db7c29-f8f6-4e98-8d65-78673fdbc1ba · outbound

This paper cites PersonaMark: Personalized LLM watermarking for model protection and user attribution.

Observation-Level Watermarking and Detection for Tabular Data PersonaMark: Personalized LLM watermarking for model protection and user attribution

Reference 25

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:b791ef923103cd26141acf897da70664dbd2fcebec9b57a9fd5bdc8b5ac08f31

Observation 53999ef1-82eb-4fed-b9ad-2e2816dd9671 · outbound

This paper cites Robust Spectral Watermark for Synthetic Tabular Data.

Observation-Level Watermarking and Detection for Tabular Data Robust Spectral Watermark for Synthetic Tabular Data

Reference 26

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:b9bbb3083d34cc9ab86c04e191b9ce472aa9d7ca103deb9d0ac00ab312e81d35

Observation d96ecb13-e0bc-46e5-b5da-d836f62e1855 · outbound

This paper cites Tabularmark: Watermarking tabular datasets for machine learning.

Observation-Level Watermarking and Detection for Tabular Data Tabularmark: Watermarking tabular datasets for machine learning

Reference 27

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:ec017a38ac1696875c32261eb16757f4b42370593b2025a5fe8800988b0a22c9

Observation a9d62761-9a6b-4bc8-87af-88380c453432 · outbound

This paper cites Tabwak: A watermark for tabular diffusion models.

Observation-Level Watermarking and Detection for Tabular Data Tabwak: A watermark for tabular diffusion models

Reference 28

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:a98f9668d7cdea9a2c5ac5870661f63c17261b3fde0df0e5d168213e0720ce33

Observation 606830df-b5c8-4c78-8e55-d3d886cfd8d7 · outbound

This paper cites Hidden: Hiding data with deep networks.

Observation-Level Watermarking and Detection for Tabular Data Hidden: Hiding data with deep networks

Reference 29

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source=pdf_text observed=2026-07-14T10:52:33.767274Z digest=sha256:338ff347d0104de55308c279523169912bc0b8c09db33322bc860cc64e43a5f9

Pith citing papers

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