Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization
- URL: http://arxiv.org/abs/2609.04970v1
- Date: Fri, 04 Sep 2026 10:22:35 GMT
- Title: Robust Coverless Linguistic Steganography via Sentence Embedding Space with Global Resynchronization
- Abstract summary: Linguistic steganography enables covert communication through natural language.<n>Existing methods rely on token-level operations and struggle to maintain reliability under word- and sentence-level perturbations.<n>We propose a robust coverless steganographic framework that operates in the sentence embedding space rather than the token space.
- Score: 13.323452988976435
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Linguistic steganography enables covert communication through natural language. Existing methods heavily rely on token-level operations and struggle to maintain reliability under word- and sentence-level textual perturbations. Moreover, variable-length coding-based schemes are highly susceptible to bit-slippage under minor disturbances, as perturbations cause desynchronization between embedded and extracted bit sequences. To address these issues, we propose a robust coverless steganographic framework that operates in the sentence embedding space rather than the token space. Specifically, secret messages are encoded as hierarchical clustering paths in the sentence embedding space, which enhances decoding stability against word- and sentence-level textual perturbations. To tackle the bit-slippage problem, we introduce a Global Resynchronization Mechanism (GRM) that reframes variable-length bitstreams as discrete symbols anchored to semantic subspaces, decoupling local embedding failures from global message recovery. Experimental results demonstrate that under word- and sentence-level perturbations, our approach achieves substantial improvements in robustness, while maintaining effective embedding capacity and exhibiting strong resistance to statistical analysis.
Related papers
- Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation [44.1310706833305]
DDB is a framework designed to resolve the fundamental misalignment of standard discrete diffusion in image translation and generation.<n>We introduce a hybrid absorption mechanism that absorbs the state to a mixture of mask and source tokens, effectively injecting source prior as spatial anchors into the latent space.<n>Experiments validate the versatility and robustness of our framework across diverse generative paradigms.
arXiv Detail & Related papers (2026-08-30T19:44:38Z) - Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation [81.21550084361755]
Language-guided Medical Image (LMIS) has shown great potential to improve the delineation of anatomical structures and lesions.<n> Existing methods rely on either implicit interaction between textual and visual features or auxiliary coarse-grained supervision for cross-modal alignment.<n>We propose Text-as-Illumination Retinex Network (TIRNet), a novel framework that treats text embeddings as semantic illumination for feature modulation.
arXiv Detail & Related papers (2026-06-26T07:27:48Z) - Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens [22.687236570529844]
We propose ASRD (Anchor Supervised Revocable Decoding), a training-free framework that operates within the embedding space.<n>We show that ASRD outperforms recent remasking baselines, achieving accuracy improvements of up to 6.4% while accelerating inference throughput by up to 7.2times$.
arXiv Detail & Related papers (2026-06-15T15:23:47Z) - PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution [51.96078493242164]
PRISM is a single-step diffusion-based Text-SR framework.<n> PRISM achieves state-of-the-art performance with millisecond-level inference.
arXiv Detail & Related papers (2026-05-13T05:31:06Z) - ReTokSync: Self-Synchronizing Tokenization Disambiguation for Generative Linguistic Steganography [28.687186883782033]
Generative linguistic steganography enables covert communication by embedding secret messages into the natural language generation process.<n>The same surface text may be re-tokenized into a different token sequence at the receiver, breaking the shared decoding state.<n>We propose ReTokSync, a framework that monitors the receiver-view tokenization during generation and triggers a corrective reset only when ambiguity actually occurs.
arXiv Detail & Related papers (2026-04-28T10:42:43Z) - STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model [71.35577462669856]
We propose a robust, provably secure linguistic steganography with diffusion language models (DLMs)<n>We introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction.
arXiv Detail & Related papers (2026-01-21T08:58:12Z) - CroBIM-U: Uncertainty-Driven Referring Remote Sensing Image Segmentation [8.834663340762562]
Referring remote sensing image segmentation aims to localize specific targets described by natural language within complex overhead imagery.<n>Existing methods typically employ uniform fusion and refinement strategies across the entire image.<n>We propose an textbfuncertainty-guided framework that explicitly leverages a pixel-wise Referrbfreferring uncertainty map as a spatial prior to orchestrate adaptive inference.
arXiv Detail & Related papers (2026-01-07T01:02:39Z) - A high-capacity linguistic steganography based on entropy-driven rank-token mapping [81.29800498695899]
Linguistic steganography enables covert communication through embedding secret messages into innocuous texts.<n>Traditional modification-based methods introduce detectable anomalies, while retrieval-based strategies suffer from low embedding capacity.<n>We propose an entropy-driven framework called RTMStega that integrates rank-based adaptive coding and context-aware decompression with normalized entropy.
arXiv Detail & Related papers (2025-10-27T06:02:47Z) - Robust Fusion Controller: Degradation-aware Image Fusion with Fine-grained Language Instructions [26.269399073437903]
Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics.<n>We propose a robust fusion controller capable of achieving degradation-aware image fusion through fine-grained language instructions.<n>Our RFC is robust against various composite degradations, particularly in highly challenging flare scenarios.
arXiv Detail & Related papers (2025-04-08T08:22:55Z) - Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion.
It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing.
Our proposed method performs favorably against other state-of-the-art algorithms.
arXiv Detail & Related papers (2024-05-15T00:17:48Z) - Discrete Variational Attention Models for Language Generation [51.88612022940496]
We propose a discrete variational attention model with categorical distribution over the attention mechanism owing to the discrete nature in languages.
Thanks to the property of discreteness, the training of our proposed approach does not suffer from posterior collapse.
arXiv Detail & Related papers (2020-04-21T05:49:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.