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Paper Citation Record · LEDGER

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models

As of 8 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.02643.

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

pith.paper-citation-record.v1
2607.02643 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T08:02:37.135363Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

24 of 24 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

Observation 36f4828e-ab96-4b44-ac76-ef33ad52a209 · outbound

This paper cites Symmetry17(7) (2025).https://doi.org/10.3390/sym17071094,https://www.mdpi.com/2073- 8994/17/7/1094, accessed: 25 Jun.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Symmetry17(7) (2025).https://doi.org/10.3390/sym17071094,https://www.mdpi.com/2073- 8994/17/7/1094, accessed: 25 Jun

Reference 1

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doi, observed 2026-07-12T08:08:37.901427Z

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:3334e3c1cd9cf2efec8ae0217d39908b51a94999c80082097bff7bd84939cbf9

Observation 367ebf2c-af1f-4f81-8520-644e88a7193a · outbound

This paper cites RoSteALS: Robust Steganography using Autoencoder Latent Space.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models RoSteALS: Robust Steganography using Autoencoder Latent Space

Reference 2

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:2681fd4f3b4c35418786219d7a19d9aa556de075bddc387beb6f9aa12fcf9832

Observation d871723a-8a6b-4c66-b70a-d2e8af3fff22 · outbound

This paper cites WMAdapter: Adding WaterMark Control to Latent Diffusion Models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models WMAdapter: Adding WaterMark Control to Latent Diffusion Models

Reference 3

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Observation 934a6340-cfe8-4003-b59b-bce6c5beea40 · outbound

This paper cites On the detection of synthetic images generated by diffusion models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models On the detection of synthetic images generated by diffusion models

Reference 4

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:dd9c795c3d8fcac3a3433575dc52706bbcd5017ee4be3f892d852c59787bd3a1

Observation 13c56845-4268-4e88-bc24-5c76a6342dd5 · outbound

This paper cites The Morgan Kaufmann Series in Multimedia Information and Systems, Morgan Kaufmann (2007),https://books.google.co.in/books?id= JZQLpzihtecC, accessed: 25 Jun.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models The Morgan Kaufmann Series in Multimedia Information and Systems, Morgan Kaufmann (2007),https://books.google.co.in/books?id= JZQLpzihtecC, accessed: 25 Jun

Reference 5

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:2a2a02d0bd139cbf5a6650a17bcddde06a1e3de8839e0ad89a8055ee45235d93

Observation 6ee5af6d-fa97-4638-afce-9760ca8d9f9d · outbound

This paper cites The Stable Signature: Rooting Watermarks in Latent Diffusion Models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models The Stable Signature: Rooting Watermarks in Latent Diffusion Models

Reference 6

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:fd9ec7fe1bc215a43fa507d8b5a23f93de8ab6436da30ad8d9609559d7642917

Observation 89fb3b7b-6a70-4d1f-ba2c-86606b8e381f · outbound

This paper cites an unresolved cited work.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Unresolved cited work

Reference 7

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Observation ebef6321-043e-4a76-9522-3ea8e5030452 · outbound

This paper cites Deep Residual Learning for Image Recognition.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Deep Residual Learning for Image Recognition

Reference 8

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Observation 77a1f16d-368d-4d92-b09f-4f8c3f856b10 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Denoising Diffusion Probabilistic Models

Reference 9

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:8dbb14b5ab8eb9ab70923f7191ae3e0708389d4aa9dbac612410b1a90fb8ca39

Observation 19f6632b-d4bc-432f-88a5-f9092041852b · outbound

This paper cites In: Multimedia Information Retrieval (2008),https://api.semanticscholar.org/CorpusID: 14040310, accessed: 25 Jun.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models In: Multimedia Information Retrieval (2008),https://api.semanticscholar.org/CorpusID: 14040310, accessed: 25 Jun

Reference 10

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Observation 7cd1f91d-1c44-4856-bcf9-0ad5b1061c1d · outbound

This paper cites Auto-Encoding Variational Bayes.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Auto-Encoding Variational Bayes

Reference 11

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Observation e2ecdc41-44fc-4093-868b-19aad922f491 · outbound

This paper cites an unresolved cited work.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Unresolved cited work

Reference 12

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:3e2a205f7dc28e318d702da777aa73bc55aa41606dde12ba9706d441decd2658

Observation b921edab-e882-44a5-9a5b-94928522c332 · outbound

This paper cites Decoupled Weight Decay Regularization.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Decoupled Weight Decay Regularization

Reference 13

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Observation 2ed9c762-3ddc-4d71-b357-8d57136c1877 · outbound

This paper cites FiLM: Visual Reasoning with a General Conditioning Layer.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models FiLM: Visual Reasoning with a General Conditioning Layer

Reference 14

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:bbd9b2a15279048594f82589c5fb5fe38706f9c3ed3b3c33571cda78915818db

Observation bfbde2cf-d1b1-4a53-8c55-d66baafd1e67 · outbound

This paper cites an unresolved cited work.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Unresolved cited work

Reference 15

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:1eda0be8023abf4c604def79fb9c7156586b8215bf257bb575973479ccf4db2f

Observation 0c40a858-d01a-40fd-b2b0-4dc57308de01 · outbound

This paper cites LaWa: Using Latent Space for In-Generation Image Watermarking.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models LaWa: Using Latent Space for In-Generation Image Watermarking

Reference 16

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:8baa3f116ea534efcd08b4e541646950d5117a8049ca1a29ab88e65caa2aa644

Observation 10cbc452-1d46-47dd-b3c6-c1f385c0112d · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 17

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Observation 4bcaedac-3c36-412e-8d4f-cc81cbeae587 · outbound

This paper cites Denoising Diffusion Implicit Models.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Denoising Diffusion Implicit Models

Reference 18

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Observation ac5be19a-add3-4170-9ce2-cf4bdcb4a92d · outbound

This paper cites for now (2020),https://arxiv.org/abs/1912.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models for now (2020),https://arxiv.org/abs/1912

Reference 19

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Observation bc40d07d-95f9-4325-84a6-bae9e113fe3b · outbound

This paper cites IEEE Transactions on Image Processing13, 600–612 (2004),https://api.semanticscholar.org/CorpusID: 207761262, accessed: 25 Jun.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models IEEE Transactions on Image Processing13, 600–612 (2004),https://api.semanticscholar.org/CorpusID: 207761262, accessed: 25 Jun

Reference 20

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:69e7a27965692ea6f32b9cdb0065a20cd04d75e147452400b3fe749ba6d628fc

Observation 0fa09745-d45f-4c7e-a488-a9cd08a2cac9 · outbound

This paper cites Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust

Reference 21

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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:576d399715fd9984d9120ac0bd14ec6927f3ae2f1569e067a4459202a7f7d402

Observation 1b7ad79b-40c7-4955-82ab-ae6c3d019445 · outbound

This paper cites Journal of Polytechnic (2023),https://api.semanticscholar.org/CorpusID: 256484136, accessed: 25 Jun.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Journal of Polytechnic (2023),https://api.semanticscholar.org/CorpusID: 256484136, accessed: 25 Jun

Reference 22

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Observation 333a1041-a5b0-4b68-9998-1585216de981 · outbound

This paper cites Robust Invisible Video Watermarking with Attention.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models Robust Invisible Video Watermarking with Attention

Reference 23

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Observation 0682fc86-4c69-40de-84e7-ca4902a749f9 · outbound

This paper cites HiDDeN: Hiding Data With Deep Networks.

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models HiDDeN: Hiding Data With Deep Networks

Reference 24

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Pith citing papers

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