Zero-shot Generative Linguistic Steganography

• URL: http://arxiv.org/abs/2403.10856v1
• Date: Sat, 16 Mar 2024 08:31:25 GMT
• Title: Zero-shot Generative Linguistic Steganography
• Authors: Ke Lin, Yiyang Luo, Zijian Zhang, Ping Luo,
• Abstract summary: We propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. Our experimental results indicate that our method produces $1.926times$ more innocent and intelligible stegotext than any other method.
• Score: 31.19052670719132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces $1.926\times$ more innocent and intelligible stegotext than any other method.

Related papers

• Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood [5.404146472517001]
  This study provides a new perspective by using the relative likelihood values instead of absolute ones. We propose a detection procedure with two classification methods, supervised and supervised-based, respectively. Our method can also reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies.
  arXiv  Detail & Related papers  (2024-06-28T12:28:52Z)
• Efficiently Leveraging Linguistic Priors for Scene Text Spotting [63.22351047545888]
  This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models. We generate text distributions that align well with scene text datasets, removing the need for in-domain fine-tuning. Experimental results show that our method not only improves recognition accuracy but also enables more accurate localization of words.
  arXiv  Detail & Related papers  (2024-02-27T01:57:09Z)
• Pixel Sentence Representation Learning [67.4775296225521]
  In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. We employ visually-grounded text perturbation methods like typos and word order shuffling, resonating with human cognitive patterns, and enabling perturbation to be perceived as continuous. Our approach is further bolstered by large-scale unsupervised topical alignment training and natural language inference supervision.
  arXiv  Detail & Related papers  (2024-02-13T02:46:45Z)
• Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition [101.60244147302197]
  We introduce contrastive learning and masked image modeling to learn discrimination and generation of text images. Our method outperforms previous self-supervised text recognition methods by 10.2%-20.2% on irregular scene text recognition datasets. Our proposed text recognizer exceeds previous state-of-the-art text recognition methods by averagely 5.3% on 11 benchmarks, with similar model size.
  arXiv  Detail & Related papers  (2022-07-01T03:50:26Z)
• Provably Secure Generative Linguistic Steganography [29.919406917681282]
  We present a novel provably secure generative linguistic steganographic method ADG. ADG embeds secret information by Adaptive Dynamic Grouping of tokens according to their probability given by an off-the-shelf language model.
  arXiv  Detail & Related papers  (2021-06-03T17:27:10Z)
• Intrinsic Probing through Dimension Selection [69.52439198455438]
  Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted.
  arXiv  Detail & Related papers  (2020-10-06T15:21:08Z)
• Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding [88.31226340759892]
  We present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. Human evaluations show that 51% of generated cover texts can indeed fool eavesdroppers.
  arXiv  Detail & Related papers  (2020-10-01T20:40:23Z)
• Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph [29.189037080306353]
  This paper proposes a new text generative steganography method which is quietly different from the existing models. We use a Knowledge Graph (KG) to guide the generation of steganographic sentences. The experimental results show that the proposed model can guarantee both the quality of the generated text and its semantic expression.
  arXiv  Detail & Related papers  (2020-06-02T06:53:21Z)
• Improving Disentangled Text Representation Learning with Information-Theoretic Guidance [99.68851329919858]
  discrete nature of natural language makes disentangling of textual representations more challenging. Inspired by information theory, we propose a novel method that effectively manifests disentangled representations of text. Experiments on both conditional text generation and text-style transfer demonstrate the high quality of our disentangled representation.
  arXiv  Detail & Related papers  (2020-06-01T03:36:01Z)

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.