CARTS: Contextual Autoregressive Rank Transcoding Steganography for Full-Capacity Keyed Text Encoding

• URL: http://arxiv.org/abs/2609.10744v1
• Date: Wed, 09 Sep 2026 18:41:11 GMT
• Title: CARTS: Contextual Autoregressive Rank Transcoding Steganography for Full-Capacity Keyed Text Encoding
• Abstract summary: Autoregressive language models can be used to transform a payload text into a stegotext of identical token length by preserving per-position rank information.<n>This paper provides the first rigorous treatment of Conpreserving Autoregressive Rank Transcoding Steganography (CARTS)<n>An empirical study on Llama 3 8B confirms exact recovery of the original payload in all tested cases.
• Score: 45.88028371034407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive language models can be used to transform a payload text into a stegotext of identical token length by preserving per-position rank information across contexts - a methodology we formalize as Contextual Autoregressive Rank Transcoding Steganography (CARTS). While the Calgacus construction of Norelli et al. demonstrated this phenomenon experimentally, no formal security analysis existed. This paper provides the first rigorous treatment of CARTS. We show its exact correctness under deterministic model assumptions, introduce a rank-coordinate representation in which keys act as bijections on rank-vector space, define relevant security notions and the computational problems naturally associated with the construction - context search, key collisions, message equivocation, and non-commutativity of the encoding maps - and study the theoretical relationships between them, including the characterization of message equivocation in terms of context search, and the tension between key collisions and message equivocation. An empirical study on Llama 3 8B confirms exact recovery of the original payload in all tested cases, finds no key collisions under random key generation, establishes that a hand-crafted collision is local rather than global, and finds no commuting key pairs - suggesting resistance to the attack vectors studied. This work opens a formally grounded research agenda for the constructive use of language models in cryptography and privacy-preserving communication.

Related papers

• Weave of Formal Thought [51.56484100374058]
  We introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations.<n>We present a latent-variable fine-tuning method training the language model to interleave non-terminal grammar symbols directly into generation.<n>For Python, fine-tuning StarCoder2-3B with our RWS objective reduces per-token cross-entropy by 14.3% relative to a text-only SFT baseline.
  arXiv  Detail & Related papers  (2026-06-24T15:58:11Z)
• PADBen: A Comprehensive Benchmark for Evaluating AI Text Detectors Against Paraphrase Attacks [2.540711742769252]
  We investigate why iteratively-paraphrased text evades detection systems designed for AIGT identification.<n>We introduce PADBen, the first benchmark systematically evaluating detector robustness against paraphrase attack scenarios.
  arXiv  Detail & Related papers  (2025-11-01T05:59:46Z)
• Context-Aware Search and Retrieval Over Erasure Channels [12.794591022795355]
  We present an information-theoretic analysis of a remote document retrieval system operating over a symbol erasure channel.<n>The proposed model encodes the feature vector of a query, derived from term-frequency weights of a language corpus.<n>We derive an explicit expression for the retrieval error probability, i.e., the probability under which the less similar document is selected.
  arXiv  Detail & Related papers  (2025-07-16T04:21:46Z)
• Unsupervised dense retrieval with conterfactual contrastive learning [16.679649921935482]
  We propose to improve the robustness of dense retrieval models by enhancing their sensitivity of fine-graned relevance signals.<n>A model achieving sensitivity in this context should exhibit high variances when documents' key passages determining their relevance to queries have been modified.<n>Motivated by causality and counterfactual analysis, we propose a series of counterfactual regularization methods.
  arXiv  Detail & Related papers  (2024-12-30T07:01:34Z)
• Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval [139.21955930418815]
  Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
  arXiv  Detail & Related papers  (2023-09-29T09:41:19Z)
• Learning-based Hybrid Local Search for the Hard-label Textual Attack [53.92227690452377]
  We consider a rarely investigated but more rigorous setting, namely hard-label attack, in which the attacker could only access the prediction label. Based on this observation, we propose a novel hard-label attack, called Learning-based Hybrid Local Search (LHLS) algorithm. Our LHLS significantly outperforms existing hard-label attacks regarding the attack performance as well as adversary quality.
  arXiv  Detail & Related papers  (2022-01-20T14:16:07Z)
• How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing [0.0]
  This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. It shows how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark.
  arXiv  Detail & Related papers  (2021-11-07T11:24:08Z)
• MASKER: Masked Keyword Regularization for Reliable Text Classification [73.90326322794803]
  We propose a fine-tuning method, coined masked keyword regularization (MASKER), that facilitates context-based prediction. MASKER regularizes the model to reconstruct the keywords from the rest of the words and make low-confidence predictions without enough context. We demonstrate that MASKER improves OOD detection and cross-domain generalization without degrading classification accuracy.
  arXiv  Detail & Related papers  (2020-12-17T04:54:16Z)
• Adversarial Semantic Collisions [129.55896108684433]
  We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions. We show how to generate semantic collisions that evade perplexity-based filtering.
  arXiv  Detail & Related papers  (2020-11-09T20:42:01Z)

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.