系统梳理语言隐写术在大模型时代五大范式转变。
A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges
- 归纳148种隐写方法与60种检测手段，构建完整分类体系。
- 揭示从人工设计到可证明安全、从对称到黑盒访问的演进。
- 为研究人员提供实用参考与负责任应用的路线图。
语言隐写术通过自然语言文本隐藏秘密信息。大型语言模型（LLMs）重塑了该领域，但当前对这些零散进展如何共同推动新范式尚缺乏系统性总结。本文从四个维度展开综述：148种隐写方法、60种语言隐写分析对抗措施、23种评估指标及9个开放挑战，均包含分类、评述与采用分析。跨维度识别出大模型时代的五项范式转变：（1）从修改原文本转向仅生成提示；（2）从启发式设计转向可证明安全性；（3）从白盒对称模型转向黑盒或非对称访问；（4）从单一安全设计转向联合优化；（5）从文本质量关注转向工程实现问题。本综述旨在为大模型时代语言隐写术的实践与负责任研究提供参考与路线图。
原文摘要 · Abstract (English)
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.
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