A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges

2026-08-29

AI Summary

本文系统总结了语言隐写术在大语言模型时代的方法、对策、评估指标及挑战，识别出五个范式转变，为该领域的实践和发展提供指导。

Abstract

Linguistic steganography hides secret messages in natural language text. Large language models (LLMs) have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic methods, 60 linguistic steganalysis countermeasures, 23 evaluation metrics, and 9 open challenges, each with taxonomies, reviews, and adoption analyses. Cutting across these axes, we identify five specific paradigm shifts in the LLM era: (1) from covertext modification to prompt-only generation, (2) from heuristic to provable security, (3) from white-box symmetric LMs to black-box or asymmetric access, (4) from security-centric designs to joint optimization, and (5) from text-quality concerns to engineering issues. The survey aims to serve as both a reference and a roadmap for practical and responsible linguistic steganography in the LLM era.

Problem

• Linguistic Steganography
• Large Language Models
• Steganographic Methods
• Steganalysis Countermeasures
• Evaluation Metrics

Innovation

• Linguistic Steganography
• Large Language Models
• Paradigm Shifts
• Provable Security
• Joint Optimization