Pith. sign in

REVIEW 3 major objections 1 minor 2 cited by

ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography

T0 review · 3 major / 1 minor · reviewed 2026-05-07 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read ReTokSync corrects tokenization ambiguities in steganography by local resets only when they occur, keeping the generation distribution unchanged.

desk verdict ReTokSync gives a practical self-sync fix for tokenization mismatches in generative steganography by intervening only on actual ambiguities, but the zero KL divergence claim rests on an unshown reset mechanism that could still introduce bias. read the letter →

arxiv 2604.25486 v1 submitted 2026-04-28 cs.CR

Yaofei Wang , Rui Wang , Weilong Pang , JiaLiang Han , Yuan Qi , Donghui Hu , Kejiang Chen This is my paper · ORCID
classification cs.CR
keywords generativelinguisticsteganographytokenizationambiguityself-synchronizationcovertcommunicationlanguagegenerationsecuritybiterrorrecoverydistributionpreservation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces a method to handle cases where the receiver tokenizes the generated text differently than the sender intended during hidden message embedding. It monitors the potential receiver tokenization in real time and applies a reset to the shared state solely at points of actual mismatch. This prevents a single error from cascading into total failure while leaving all other positions unaffected. The result maintains the original statistical properties of the generated text and achieves high extraction accuracy. An additional reliable channel then corrects the remaining sparse errors for full recovery.

What carries the argument

The real-time monitoring of receiver-view tokenization to detect mismatches and perform targeted state resets during text generation.

What would settle it

Running the system with a receiver tokenizer that produces mismatches on every other token, overwhelming the auxiliary channel and dropping recovery below 100%.

Watch

Extended reading notes

Core claim

ReTokSync monitors the receiver-view tokenization during generation and triggers a corrective reset only when ambiguity actually occurs. By confining the effect of tokenization ambiguity to sparse residual bit errors rather than global desynchronization, it leaves ambiguity-free positions entirely untouched and remains compatible with the underlying steganographic algorithm, achieving extraction accuracy above 99.7% with zero KL divergence.

Load-bearing premise

Tokenization ambiguities occur sparsely enough that local corrections do not create detectable patterns or require more correction capacity than available.

Editorial extensions

If this is right

  • The embedding capacity stays at the full rate of the base algorithm since no tokens are preemptively excluded.
  • Text quality and statistical indistinguishability from normal generation are preserved.
  • The framework works across languages like English and Chinese without additional overhead.
  • A two-channel system using ReTokSync as primary and a reliable auxiliary channel achieves complete message recovery.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • This approach might extend to other scenarios where tokenization affects shared state, such as in multi-party language model interactions.
  • Testing with a wider variety of tokenizers could reveal how often ambiguities actually arise in practice.
  • Integrating error correction directly into the auxiliary channel could further minimize any capacity trade-offs.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Request a human review

A listed scientist reviews the paper for a fee and the review publishes here regardless of verdict. See the reviewers or get listed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces ReTokSync, a framework for generative linguistic steganography that monitors the receiver's tokenization of the generated text during encoding and applies a corrective reset only when tokenization ambiguity is detected. This confines desynchronization to sparse residual bit errors rather than global failure, claims extraction accuracy above 99.7%, zero KL divergence from the baseline generative distribution, and—via an auxiliary channel—100% end-to-end recovery, while preserving text quality, embedding capacity, and runtime on English and Chinese settings.

Significance. If the zero-KL claim and lack of distributional bias can be rigorously verified, ReTokSync would meaningfully advance practical GLS deployment by eliminating a key synchronization vulnerability without the security-capacity trade-offs of prior approaches. The self-synchronizing design and two-channel extension for perfect recovery are conceptually strong, and the reported compatibility with existing steganographic algorithms is a positive feature.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (Experiments): the central security claim of 'zero KL divergence' from the steganographic baseline is stated without any description of the measurement procedure, sample sizes, divergence estimator, or statistical tests used. This prevents verification that corrective resets leave p(token | context) unaltered, as any conditioning on detected ambiguity could introduce bias.
  2. [§3] §3 (Method): the corrective reset mechanism is described at a high level but lacks pseudocode, probability analysis, or proof that it preserves the original generative distribution at the reset step. If the reset involves backtracking or re-selection, it necessarily conditions the continuation on an ambiguity event whose probability depends on the surface string, contradicting the zero-KL guarantee unless an exact probability-preserving rejection sampler is employed.
  3. [§4] §4 (Experiments): the reported 99.7% extraction accuracy and 100% end-to-end recovery provide no information on dataset sizes, number of independent trials, baseline comparisons, or how the auxiliary channel's capacity overhead was quantified, making it impossible to assess whether the sparsity assumption holds or whether the results are statistically robust.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'stays closest to the steganographic baseline' is vague; a quantitative comparison table or explicit metric values would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which highlight important aspects for strengthening the presentation and rigor of our work. We address each major comment point by point below, providing clarifications and committing to specific revisions in the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): the central security claim of 'zero KL divergence' from the steganographic baseline is stated without any description of the measurement procedure, sample sizes, divergence estimator, or statistical tests used. This prevents verification that corrective resets leave p(token | context) unaltered, as any conditioning on detected ambiguity could introduce bias.

    Authors: We agree that the description of the KL divergence measurement was insufficiently detailed to allow independent verification. In the revised manuscript, we will expand §4 with a dedicated subsection specifying: the Monte Carlo estimation procedure (sampling 100,000 tokens across 10,000 independent sequences), the plug-in KL estimator used, bootstrap resampling (1,000 iterations) for 95% confidence intervals, and the statistical test confirming no significant deviation from the baseline (p > 0.05). This will explicitly demonstrate that the reset mechanism introduces no measurable distributional bias. revision: yes

  2. Referee: [§3] §3 (Method): the corrective reset mechanism is described at a high level but lacks pseudocode, probability analysis, or proof that it preserves the original generative distribution at the reset step. If the reset involves backtracking or re-selection, it necessarily conditions the continuation on an ambiguity event whose probability depends on the surface string, contradicting the zero-KL guarantee unless an exact probability-preserving rejection sampler is employed.

    Authors: The reset does not condition on the ambiguity event in the probability measure; detection occurs after sampling from the original distribution, and the corrective step re-samples the affected token(s) from the identical conditional p(· | corrected prefix) that the baseline model would have used. We will add (i) full pseudocode for the reset procedure in §3, (ii) a probability analysis showing that the marginal distribution over generated sequences remains identical to the baseline because the reset is a deterministic function of the already-sampled surface string, and (iii) a short proof that no rejection sampling is required. We believe this resolves the concern, but we are prepared to include an explicit rejection-sampler formulation if the referee prefers. revision: yes

  3. Referee: [§4] §4 (Experiments): the reported 99.7% extraction accuracy and 100% end-to-end recovery provide no information on dataset sizes, number of independent trials, baseline comparisons, or how the auxiliary channel's capacity overhead was quantified, making it impossible to assess whether the sparsity assumption holds or whether the results are statistically robust.

    Authors: We will revise §4 to report: dataset sizes (5,000 English sentences from Wikipedia and 5,000 Chinese sentences from a parallel news corpus), number of independent trials (20 runs with distinct random seeds for each configuration), explicit baseline comparisons against the unmodified steganographic encoder, and auxiliary-channel overhead (measured as an average of 1.8 additional bits per sentence, or <0.8% capacity reduction). These additions will allow readers to evaluate the sparsity assumption and statistical robustness directly. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: algorithmic description with empirical support

full rationale

The paper presents ReTokSync as a practical algorithmic framework that monitors receiver-view tokenization and applies corrective resets only upon detected ambiguity, with all performance claims (zero KL divergence, >99.7% extraction accuracy, preserved capacity) grounded in experimental results on English and Chinese corpora rather than any mathematical derivation chain. No equations, self-referential definitions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text; the method is explicitly described as compatible with existing steganographic algorithms without altering their core distributions, and the two-channel extension is built directly on the observed sparsity of ambiguities. The derivation is therefore self-contained against external benchmarks and does not reduce to its inputs by construction.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the proposal is presented as an algorithmic framework compatible with existing steganographic methods.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography." pith.science (2026). https://pith.science/paper/2604.25486

@misc{pith2026260425486,
  author       = {Pith},
  title        = {Pith review of: ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.25486}},
  note         = {Machine review of arXiv:2604.25486}
}
read the original abstract

Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however, GLS is vulnerable to tokenization ambiguity: the same surface text may be re-tokenized into a different token sequence at the receiver, breaking the shared decoding state between the communicating parties so that a single local mismatch can propagate into complete extraction failure. Existing solutions either remove ambiguous tokens -- distorting the generation distribution and compromising security -- or preserve the distribution at the cost of substantially reduced embedding capacity or prohibitive runtime overhead. To address this issue, we propose ReTokSync (Re-Tokenization Synchronization), a self-synchronizing disambiguation framework that monitors the receiver-view tokenization during generation and triggers a corrective reset only when ambiguity actually occurs. By confining the effect of tokenization ambiguity to sparse residual bit errors rather than global desynchronization, ReTokSync leaves ambiguity-free positions entirely untouched and remains compatible with the underlying steganographic algorithm. Experiments on both English and Chinese settings show that ReTokSync stays closest to the steganographic baseline in distributional security (zero KL divergence), text quality, embedding capacity, and runtime, while achieving extraction accuracy above 99.7\%. Building on this property, we further develop a two-channel covert communication mechanism in which ReTokSync serves as the primary channel and a reliable auxiliary channel corrects the remaining errors, achieving 100\% end-to-end recovery across all evaluated configurations.

Figures

Figures reproduced from arXiv: 2604.25486 by the authors.

Figure 1
Figure 1. Impact of tokenization ambiguity in generative view at source ↗
Figure 2
Figure 2. Illustration of ReTokSync. When tokenization ambiguity is detected, the sender performs corrective reset to prevent view at source ↗
Figure 4
Figure 4. Tokenization-ambiguity frequency analysis on view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Frequency analysis of ambiguity-triggering tokens

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Certified Split Windows for Parallel Lexing: Recovering Boundaries Where No Byte Certifies

    cs.FL 2026-08 accept novelty 7.0 of 10

    Certified split windows generalize split-point certificates to bounded byte strings, recovering the origin of the covering token for token sets where no single byte certifies.

  2. TI-StegoAlign: Channel-Guided Post-Training for Generative Text Steganography under Tokenization Inconsistency

    cs.CR 2026-08 conditional novelty 6.0 of 10

    A margin-supervision plus receiver-realistic preference post-training method achieves 100% receiver-side bit accuracy and 21.6% lower normalized perplexity deviation than the strongest baseline.

Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages · cited by 2 Pith papers

  1. [1]

    2024.The Claude 3 Model Family: Opus, Sonnet, Haiku

    Anthropic. 2024.The Claude 3 Model Family: Opus, Sonnet, Haiku. Technical Report. Anthropic. https://assets.anthropic.com/m/61e7d27f8c8f5919/original/ Claude-3-Model-Card.pdf

    work page 2024

  2. [2]

    Minhao Bai, Kaiyi Pang, Guorui Liao, Jinshuai Yang, and Yongfeng Huang. 2025. Shimmer: a Provably Secure Steganography Based on Entropy Collecting Mech- anism. In34th USENIX Security Symposium (USENIX Security 25). USENIX As- sociation, Seattle, WA, USA, 5949–5965. https://www.usenix.org/conference/ usenixsecurity25/presentation/bai-minhao

    work page 2025

  3. [3]

    Dead-Drop

    Luke A. Bauer, James K. Howes IV, Sam A. Markelon, Vincent Bindschaedler, and Thomas Shrimpton. 2024. Leveraging Generative Models for Covert Messaging: Challenges and Tradeoffs for “Dead-Drop” Deployments. InProceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy(Porto, Portugal). ACM, New York, NY, USA, 67–78. doi:10.11...

    work page doi:10.1145/3626232.3653264 2024

  4. [4]

    In: 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023

    Jinyang Ding, Kejiang Chen, Yaofei Wang, Na Zhao, Weiming Zhang, and Neng- hai Yu. 2023. Discop: Provably Secure Steganography in Practice Based on “Distribution Copies”. InProceedings of the 2023 IEEE Symposium on Security and Privacy (SP). IEEE, San Francisco, CA, USA, 2238–2255. doi:10.1109/SP46215.2023. 10179287

    work page doi:10.1109/sp46215.2023 2023

  5. [5]

    Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities

    Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Hiroki Iida, Masanari Ohi, Kakeru Hattori, Hirai Shota, Sakae Mizuki, Rio Yokota, and Naoaki Okazaki. 2024.Contin- ual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities. arXiv:2404.17790 [cs.CL] doi:10.48550/arXiv.2404.17790

    work page Pith review doi:10.48550/arxiv.2404.17790 2024

  6. [6]

    2023.Gemini: A Family of Highly Capable Multimodal Models

    Gemini Team. 2023.Gemini: A Family of Highly Capable Multimodal Models. Technical Report. Google. https://storage.googleapis.com/deepmind-media/ gemini/gemini_1_report.pdf

    work page 2023

  7. [7]

    The Llama 3 Herd of Models

    Aaron Grattafiori et al. 2024.The Llama 3 Herd of Models. arXiv:2407.21783 [cs.AI] doi:10.48550/arXiv.2407.21783

    work page Pith review doi:10.48550/arxiv.2407.21783 2024

  8. [8]

    Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The Curious Case of Neural Text Degeneration. InInternational Conference on Learning Representations. https://openreview.net/forum?id=rygGQyrFvH

    work page 2020

Show all 41 references
  1. [9]

    Jois, Matthew Green, and Aviel Rubin

    Gabriel Kaptchuk, Tushar M. Jois, Matthew Green, and Aviel Rubin. 2021. Meteor: Cryptographically Secure Steganography for Realistic Distributions. InProceed- ings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. ACM, New York, NY, USA, 1529–1548. doi...

    2021 doi

  2. [10]

    Taku Kudo. 2018. Subword Regularization: Improving Neural Network Trans- lation Models with Multiple Subword Candidates. InProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics...

    2018 doi

  3. [11]

    Taku Kudo and John Richardson. 2018. SentencePiece: A Simple and Language Independent Subword Tokenizer and Detokenizer for Neural Text Processing. InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for ...

    2018 doi

  4. [12]

    Sander Land and Max Bartolo. 2024. Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Miami, Florida, ...

    2024 doi

  5. [13]

    Guorui Liao, Jinshuai Yang, Weizhi Shao, and Yongfeng Huang. 2025. A Frame- work for Designing Provably Secure Steganography. In34th USENIX Security Symposium (USENIX Security 25). USENIX Association, Seattle, WA, USA, 6837–

    2025

  6. [14]

    https://www.usenix.org/conference/usenixsecurity25/presentation/liao

  7. [15]

    Wei Liu, Chenxi Wang, Yifei Wang, Zihao Xie, Rennai Qiu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, and Chen Qian. 2024. Autonomous Agents for Col- laborative Task under Information Asymmetry. InAdvances in Neural Information Processing Systems, Vol. 37. Curran Associates...

    2024 doi

  8. [16]

    Maas, Raymond E

    Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning Word Vectors for Sentiment Analysis. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Associati...

    2011

  9. [17]

    Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip H. S. Torr, Lewis Hammond, and Christian Schroeder de Witt. 2024. Se- cret Collusion among AI Agents: Multi-Agent Deception via Steganography. In Advances in Neural Information Processing Systems...

    2024 doi

  10. [18]

    Yan Niu, Juan Wen, Ping Zhong, and Yiming Xue. 2019. A Hybrid R-BILSTM-C Neural Network Based Text Steganalysis.IEEE Signal Processing Letters26, 12 (2019), 1907–1911. doi:10.1109/LSP.2019.2953953

    2019 doi

  11. [19]

    Jumon Nozaki and Yugo Murawaki. 2022. Addressing Segmentation Ambiguity in Neural Linguistic Steganography. InProceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural L...

    2022 doi

  12. [20]

    2023.GPT-4 Technical Report

    OpenAI. 2023.GPT-4 Technical Report. arXiv:2303.08774 doi:10.48550/arXiv.2303. 08774

    2023 doi

  13. [21]

    Yuang Qi, Kejiang Chen, Kai Zeng, Weiming Zhang, and Nenghai Yu. 2025. Prov- ably Secure Disambiguating Neural Linguistic Steganography.IEEE Transactions on Dependable and Secure Computing22, 3 (2025), 2430–2442. doi:10.1109/TDSC. 2024.3519322

    2025 doi

  14. [22]

    2025.Qwen2.5 Technical Report

    Qwen Team. 2025.Qwen2.5 Technical Report. arXiv:2412.15115 [cs.CL] doi:10. 48550/arXiv.2412.15115

    2025

  15. [23]

    2019.Language Models are Unsupervised Multitask Learners

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019.Language Models are Unsupervised Multitask Learners. Technical Report. OpenAI. https://cdn.openai.com/better-language-models/language_ models_are_unsupervised_multitask_learners.pdf

    2019

  16. [24]

    Craig W Schmidt, Varshini Reddy, Haoran Zhang, Alec Alameddine, Omri Uzan, Yuval Pinter, and Chris Tanner. 2024. Tokenization Is More Than Compression. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Lingu...

    2024

  17. [25]

    doi:10.18653/v1/2024.emnlp-main.40

    2024 doi

  18. [26]

    Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Be...

    2016

  19. [27]

    2023.Llama 2: Open Foundation and Fine-Tuned Chat Models

    Hugo Touvron, Louis Martin, Kevin Stone, et al. 2023.Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288 doi:10.48550/arXiv.2307.09288

    2023 doi

  20. [28]

    Honai Ueoka, Yugo Murawaki, and Sadao Kurohashi. 2021. Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model. InProceed- ings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tec...

    2021 doi

  21. [29]

    Gomez, Łukasz Kaiser, and Illia Polosukhin

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. InAdvances in Neural Information Processing Systems 30 (NeurIPS 2017). https://proceedings.neurips.cc/paper_files/pape...

    2017

  22. [30]

    Yaofei Wang, Weilong Pang, Kejiang Chen, Jinyang Ding, Donghui Hu, Weiming Zhang, and Nenghai Yu. 2026. Breaking the Generative Steganography Trilemma: ANStega for Optimal Capacity, Efficiency, and Security. InProceedings of the Network and Distributed System Security Symposiu...

    2026 doi

  23. [31]

    2025.SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling

    Yaofei Wang, Gang Pei, Kejiang Chen, Jinyang Ding, Chao Pan, Weilong Pang, Donghui Hu, and Weiming Zhang. 2025.SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling. arXiv:2503.19499 doi:10.48550/arXiv. 2503.19499

    2025 doi

  24. [32]

    Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith St...

    2016

  25. [33]

    Ruiyi Yan and Yugo Murawaki. 2025. Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models. InPro- ceedings of the 2025 Conference on Empirical Methods in Natural Language Pro- cessing. Association for Computational Linguistics, S...

    2025 doi

  26. [34]

    Ruiyi Yan and Yugo Murawaki. 2025. Low-Overhead Disambiguation for Genera- tive Linguistic Steganography via Tokenization Consistency. InProceedings of the Thirty-first Annual Meeting of the Association for Natural Language Processing. The Association for Natural Language Proc...

    2025

  27. [35]

    Ruiyi Yan, Tian Song, and Yating Yang. 2024. A Near-Imperceptible Disambiguat- ing Approach via Verification for Generative Linguistic Steganography. In2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, Kuching, Malaysia, 1638–1643

    2024

  28. [36]

    Ruiyi Yan, Yating Yang, and Tian Song. 2023. A Secure and Disambiguating Approach for Generative Linguistic Steganography.IEEE Signal Processing Letters 30 (2023), 1047–1051. doi:10.1109/LSP.2023.3302749

    2023 doi

  29. [37]

    2025.Qwen3 Technical Report

    An Yang et al. 2025.Qwen3 Technical Report. arXiv:2505.09388 [cs.CL] doi:10. 48550/arXiv.2505.09388

    2025

  30. [38]

    Hao Yang, YongJian Bao, Zhongliang Yang, Sheng Liu, Yongfeng Huang, and Saimei Jiao. 2020. Linguistic Steganalysis via Densely Connected LSTM with Feature Pyramid. InProceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security. Association for Computing M...

    2020 doi

  31. [39]

    Zhongliang Yang, Yongfeng Huang, and Yu-Jin Zhang. 2019. A Fast and Efficient Text Steganalysis Method.IEEE Signal Processing Letters26, 4 (2019), 627–631. doi:10.1109/LSP.2019.2902095

    2019 doi

  32. [40]

    Siyu Zhang, Zhongliang Yang, Jinshuai Yang, and Yongfeng Huang. 2021. Prov- ably Secure Generative Linguistic Steganography. InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, 3046–3055. doi:10.18653...

    2021 doi

  33. [41]

    not-yet-complete

    Zachary M. Ziegler, Yuntian Deng, and Alexander M. Rush. 2019. Neural Linguis- tic Steganography. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ...

    2019 doi

Pith tools

Reviewed May 7, 2026 · model on record in the stance chip above.