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Paper Citation Record · LEDGER

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models

As of 10 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

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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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:e77a519623bde23f5ff136bd8d0745f948f1ff915897a0eec990743e482a7235

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=sha256:6aee78c927c9815e382fb17481cb5e9bbe51cb6db6f0595a44c44122d0928bd1

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=sha256:06ec922497f07451241d75facda390ca574cca193c8786c6b4e59150e35611a6

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=sha256:71bd1085e54f3d3f836ec15949c85fdf53b65e74de20d752688534dbfa9c524d

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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source=pdf_text observed=2026-07-12T08:02:37.135363Z digest=sha256:3097a1b2b2a7479b4085e0f84197036100cc0c05c85c8dfbee28f3761a074fdc

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=sha256:28a1d4175993721f88db78b70ac58637f7d3f0e9bfd1e61d67eee6b67dc39fde

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:a1ce77a92df05b11b5c305134df248d190cd83420cd56eb204b216bdb7be5eac

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:c4be1b9ee73ed65b4855ead9acee2e8d3ec1df285e8ce8bd2ae7567f4ccfe032

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=sha256:74530915d44d668acdd7538f0fbbe12821ad9b3cc97f78c3bb3dde1b311ba834

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=sha256:291101edff5583192d8dd708865297ec326e1194f6a6930b7bc61696d10d31ea

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=sha256:242af2a06a045ef7b5c7e8ab01272664ce9ece0d0c2fa1a62ac0f5aebf66c907

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:f1324a5d47b32903b80452885257b36d2528dc10b3d4acb38f7a2b92bc639ff9

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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