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Paper Citation Record · LEDGER

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2606.07655.

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

pith.paper-citation-record.v1
2606.07655 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T05:25:40.512453Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

  • verified exact4
  • verified fuzzy0
  • unresolved28
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 79d0cb12-6f6e-4836-9634-783e319274c5 · outbound

This paper cites an unresolved cited work.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Unresolved cited work

Reference 1

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Observation 841db021-00ab-4a24-bfa4-34dc3a7ffd88 · outbound

This paper cites Towards empowering cyber attack resiliency using steganography,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Towards empowering cyber attack resiliency using steganography,

Reference 2

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Observation ef6fe8ca-ea21-402d-a8d7-969563eb8a93 · outbound

This paper cites RNN-Stega: Linguistic steganography based on recurrent neural net- works,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis RNN-Stega: Linguistic steganography based on recurrent neural net- works,

Reference 3

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Observation ffe9098c-c576-42bc-833a-d6f4ddc8d92e · outbound

This paper cites Neural linguistic steganography,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Neural linguistic steganography,

Reference 4

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Observation 56620768-34d4-4491-9fdb-226b5d21403a · outbound

This paper cites V AE- Stega: Linguistic steganography based on variational auto-encoder,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis V AE- Stega: Linguistic steganography based on variational auto-encoder,

Reference 5

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Observation 1a64173c-636a-4de6-886f-78d881b7721c · outbound

This paper cites Zero-shot Generative Linguistic Steganography.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Zero-shot Generative Linguistic Steganography

Reference 6

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arxiv_id, observed 2026-07-02T09:46:49.999913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 5a69fc66-8523-421d-9a51-44c935f6ddd7 · outbound

This paper cites Generative Text Steganography with Large Language Model.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Generative Text Steganography with Large Language Model

Reference 7

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arxiv_id, observed 2026-07-02T09:46:49.996922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation ea996555-52a3-4bb4-b70e-43a08f4dc87d · outbound

This paper cites Discop: Provably secure steganography in practice based on distribution copies,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Discop: Provably secure steganography in practice based on distribution copies,

Reference 8

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Observation b2c7f801-cec1-457a-9f54-4eadd6d43a1c · outbound

This paper cites A framework for designing provably secure steganography,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis A framework for designing provably secure steganography,

Reference 9

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source=pdf_text observed=2026-06-28T05:25:40.512453Z digest=sha256:ad7db2cb97d4b32957fdd4ea671dd7178c41995fe55fac5bfef8e09b787e43d2

Observation f0a2ef49-22bf-4315-abee-23432572d113 · outbound

This paper cites Shimmer: A provably secure steganography based on entropy collecting mechanism,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Shimmer: A provably secure steganography based on entropy collecting mechanism,

Reference 10

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source=pdf_text observed=2026-06-28T05:25:40.512453Z digest=sha256:81061665c53fd8f142aa31237a1a028fa0645311e61279bee7d239f76fa97f67

Observation a1b6aa3c-fb69-417b-bdaa-25287a11de29 · outbound

This paper cites SparSamp: Efficient provably secure steganography based on sparse sampling,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis SparSamp: Efficient provably secure steganography based on sparse sampling,

Reference 11

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Observation 3aca15cb-dc38-4bbc-8a03-db2375254775 · outbound

This paper cites Rethinking prefix-based steganography for enhanced security and efficiency,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Rethinking prefix-based steganography for enhanced security and efficiency,

Reference 12

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Observation d37c0ff1-f56f-4b49-aba5-7ed51405c2f2 · outbound

This paper cites Steganalysis against substitution-based linguistic steganography based on context clusters,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Steganalysis against substitution-based linguistic steganography based on context clusters,

Reference 13

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Observation 45ce517e-c557-49a7-9319-b75a5838c845 · outbound

This paper cites Linguistic steganalysis using the features derived from synonym frequency,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Linguistic steganalysis using the features derived from synonym frequency,

Reference 14

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Source-reported events for the cited work

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source=pdf_text observed=2026-06-28T05:25:40.512453Z digest=sha256:e5381b8cf69744e1df31327ed06ef4cbfe8dc36d0467c9b570eb35daabea1b0e

Observation d6d46b0b-82f3-4b2b-9d91-adf74d65da36 · outbound

This paper cites TS-RNN: Text steganalysis based on recurrent neural networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis TS-RNN: Text steganalysis based on recurrent neural networks,

Reference 15

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Source-reported events for the cited work

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source=pdf_text observed=2026-06-28T05:25:40.512453Z digest=sha256:30f5c49aee5e89001f09e26c9b059eae893cc95aef4fdb41ea949b8a7f067e19

Observation e5a8ed9d-f2a9-4edb-b54a-fc741e78560c · outbound

This paper cites TS-CSW: Text steganalysis and hidden capacity estimation based on convolutional sliding windows,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis TS-CSW: Text steganalysis and hidden capacity estimation based on convolutional sliding windows,

Reference 16

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Observation e9e46306-44bf-4541-b1a6-bf9b01cf3841 · outbound

This paper cites A fast and efficient text steganal- ysis method,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis A fast and efficient text steganal- ysis method,

Reference 17

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Observation 0027c36c-b2c6-4046-abe9-2059e3c9e58b · outbound

This paper cites Linguistic steganalysis with graph neural networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Linguistic steganalysis with graph neural networks,

Reference 18

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Observation fa558e24-c55c-44d6-9535-c72da56eb072 · outbound

This paper cites Text steganalysis based on hierarchical supervised learning and dual attention mechanism,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Text steganalysis based on hierarchical supervised learning and dual attention mechanism,

Reference 19

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Observation b165d94d-58f0-4105-9737-9b23cab87433 · outbound

This paper cites Linguistic steganalysis toward social network,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Linguistic steganalysis toward social network,

Reference 20

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Observation fcde515d-aba0-4e53-874b-9828c8664736 · outbound

This paper cites LINK: Linguistic steganalysis framework with external knowledge,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis LINK: Linguistic steganalysis framework with external knowledge,

Reference 21

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Observation 328e3ff8-9971-4c56-91dc-aedf1c0648a6 · outbound

This paper cites CATS: Connection-aware and interaction-based text steganalysis in social networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis CATS: Connection-aware and interaction-based text steganalysis in social networks,

Reference 22

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Observation 96805f90-3ce1-4e76-9c75-96c86fceb1d1 · outbound

This paper cites TGCA: A transformer GNN-based approach with cross-attention mechanism for steganographic text detection in social networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis TGCA: A transformer GNN-based approach with cross-attention mechanism for steganographic text detection in social networks,

Reference 23

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Observation 18ed05e1-ba0d-4d74-8102-00017cd59e36 · outbound

This paper cites STLC-KG: A social text steganalysis method combining large-scale language models and common-sense knowledge graphs,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis STLC-KG: A social text steganalysis method combining large-scale language models and common-sense knowledge graphs,

Reference 24

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Observation 6657b41a-99c7-49cf-9da0-1b2cd0316783 · outbound

This paper cites Context-aware and semantic-synergistic linguistic steganalysis for social networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Context-aware and semantic-synergistic linguistic steganalysis for social networks,

Reference 25

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source=pdf_text observed=2026-06-28T05:25:40.512453Z digest=sha256:652d299acee881dbac808ec9a1ac00bd16b4eaf484e0f30aab1e0dd042a8e0ce

Observation f611802e-1260-4122-8acb-222c63e22d30 · outbound

This paper cites User profile constructed by multiple attributes for optimizing linguistic steganalysis in social networks,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis User profile constructed by multiple attributes for optimizing linguistic steganalysis in social networks,

Reference 26

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Unavailable: canonical work link unavailable.

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Observation 67071803-65e4-49d5-a36e-3f7fdaf3a18b · outbound

This paper cites Aggregated text steganaly- sis toward social network based on efficient multi-perspective feature fusion,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Aggregated text steganaly- sis toward social network based on efficient multi-perspective feature fusion,

Reference 27

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Observation fccd81f2-c27b-4499-9650-d17b8c2aaf92 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Learning imbalanced datasets with label-distribution-aware margin loss,

Reference 28

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Observation 9d29aa10-7be6-47a0-8d19-3af6771286d3 · outbound

This paper cites Focal loss for dense object detection,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Focal loss for dense object detection,

Reference 29

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Observation 58ad7cb3-504a-4061-aed9-25319a0cd346 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 30

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local_arxiv, observed 2026-07-02T09:46:50.002067Z

Source-reported events for the cited work

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Observation c116a823-31f5-4a0e-b8fa-6c05e8000973 · outbound

This paper cites Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding

Reference 31

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local_arxiv, observed 2026-07-02T09:46:50.005384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 77de92b0-2fcd-44ae-8fa4-185f4505b313 · outbound

This paper cites Provably secure generative linguistic steganography,.

FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis Provably secure generative linguistic steganography,

Reference 32

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