STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model

• URL: http://arxiv.org/abs/2601.14778v1
• Date: Wed, 21 Jan 2026 08:58:12 GMT
• Title: STEAD: Robust Provably Secure Linguistic Steganography with Diffusion Language Model
• Authors: Yuang Qi, Na Zhao, Qiyi Yao, Benlong Wu, Weiming Zhang, Nenghai Yu, Kejiang Chen,
• Abstract summary: 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.
• Score: 71.35577462669856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent provably secure linguistic steganography (PSLS) methods rely on mainstream autoregressive language models (ARMs) to address historically challenging tasks, that is, to disguise covert communication as ``innocuous'' natural language communication. However, due to the characteristic of sequential generation of ARMs, the stegotext generated by ARM-based PSLS methods will produce serious error propagation once it changes, making existing methods unavailable under an active tampering attack. To address this, we propose a robust, provably secure linguistic steganography with diffusion language models (DLMs). Unlike ARMs, DLMs can generate text in a partially parallel manner, allowing us to find robust positions for steganographic embedding that can be combined with error-correcting codes. Furthermore, we introduce error correction strategies, including pseudo-random error correction and neighborhood search correction, during steganographic extraction. Theoretical proof and experimental results demonstrate that our method is secure and robust. It can resist token ambiguity in stegotext segmentation and, to some extent, withstand token-level attacks of insertion, deletion, and substitution.

Related papers

• Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors [77.82885394684202]
  We propose textbfContrastive textbfParaphrase textbfAttack (CoPA), a training-free method that effectively deceives text detectors.<n>CoPA constructs an auxiliary machine-like word distribution as a contrast to the human-like distribution generated by large language models.<n>Our theoretical analysis suggests the superiority of the proposed attack.
  arXiv  Detail & Related papers  (2025-05-21T10:08:39Z)
• Robust Steganography from Large Language Models [1.5749416770494704]
  We study the problem of robust steganography.<n>We design and implement our steganographic schemes that embed arbitrary secret messages into natural language text.
  arXiv  Detail & Related papers  (2025-04-11T21:06:36Z)
• Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attacks [50.53590930588431]
  adversarial examples pose serious threats to natural language processing systems.<n>Recent studies suggest that adversarial texts deviate from the underlying manifold of normal texts, whereas masked language models can approximate the manifold of normal data.<n>We first introduce Masked Language Model-based Detection (MLMD), leveraging mask unmask operations of the masked language modeling (MLM) objective.
  arXiv  Detail & Related papers  (2025-04-08T14:10:57Z)
• FreStega: A Plug-and-Play Method for Boosting Imperceptibility and Capacity in Generative Linguistic Steganography for Real-World Scenarios [0.0]
  Linguistic steganography embeds secret information in seemingly innocent texts, safeguarding privacy in surveillance environments.<n>We propose FreStega, a plug-and-play method to reconstruct the distribution of language models used for generative linguistic steganography.
  arXiv  Detail & Related papers  (2024-12-27T13:56:51Z)
• Provably Secure Disambiguating Neural Linguistic Steganography [66.30965740387047]
  The segmentation ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding failures.<n>We propose a novel secure disambiguation method named SyncPool, which effectively addresses the segmentation ambiguity problem.<n> SyncPool does not change the size of the candidate pool or the distribution of tokens and thus is applicable to provably secure language steganography methods.
  arXiv  Detail & Related papers  (2024-03-26T09:25:57Z)
• Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks [39.51297217854375]
  We propose Text-CRS, a certified robustness framework for natural language processing (NLP) based on randomized smoothing. We show that Text-CRS can address all four different word-level adversarial operations and achieve a significant accuracy improvement. We also provide the first benchmark on certified accuracy and radius of four word-level operations, besides outperforming the state-of-the-art certification against synonym substitution attacks.
  arXiv  Detail & Related papers  (2023-07-31T13:08:16Z)
• Towards Imperceptible Document Manipulations against Neural Ranking Models [13.777462017782659]
  We propose a framework called Imperceptible DocumEnt Manipulation (IDEM) to produce adversarial documents. IDEM instructs a well-established generative language model, such as BART, to generate connection sentences without introducing easy-to-detect errors. We show that IDEM can outperform strong baselines while preserving fluency and correctness of the target documents.
  arXiv  Detail & Related papers  (2023-05-03T02:09:29Z)
• Contextualized Perturbation for Textual Adversarial Attack [56.370304308573274]
  Adversarial examples expose the vulnerabilities of natural language processing (NLP) models. This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs.
  arXiv  Detail & Related papers  (2020-09-16T06:53:15Z)
• BERT-ATTACK: Adversarial Attack Against BERT Using BERT [77.82947768158132]
  Adrial attacks for discrete data (such as texts) are more challenging than continuous data (such as images) We propose textbfBERT-Attack, a high-quality and effective method to generate adversarial samples. Our method outperforms state-of-the-art attack strategies in both success rate and perturb percentage.
  arXiv  Detail & Related papers  (2020-04-21T13:30:02Z)

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.