A Semantic Controllable Long Text Steganography Framework Based on LLM Prompt Engineering and Knowledge Graph

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-09 DOI:10.1109/LSP.2024.3456636
Yihao Li;Ru Zhang;Jianyi Liu;Qi Lei
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引用次数: 0

Abstract

With ongoing advancements in natural language technology, text steganography has achieved notable progress. However, existing methods primarily concentrate on the probability distribution between words, often overlooking comprehensive control over text semantics. Particularly in the case of longer texts, these methods struggle to preserve coherence and contextual consistency, thereby increasing the risk of detection in practical applications. To effectively improve steganography security, we propose a semantic controllable long-text steganography framework based on prompt engineering and knowledge graph (KG) integration, obviating supplementary training. This framework leverages triplets from the KG and task descriptions to construct prompts, directing the large language model (LLM) to generate text that aligns with the triplet content. Subsequently, the model effectively embeds secret information by encoding the candidate pools established around the sampled target words. The experimental results demonstrate that our framework ensures the concealment of steganographic text while maintaining the relevance and consistency of the content as expected. Moreover, it can be flexibly adapted to various application scenarios, showcasing its potential and advantages in practical implementations.
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基于 LLM 提示工程和知识图谱的语义可控长文本隐写术框架
随着自然语言技术的不断进步,文本隐写术取得了显著进展。然而,现有的方法主要集中在词与词之间的概率分布上,往往忽略了对文本语义的全面控制。特别是对于较长的文本,这些方法很难保持连贯性和上下文的一致性,从而增加了实际应用中被检测到的风险。为了有效提高隐写术的安全性,我们提出了一种基于提示工程和知识图谱(KG)集成的语义可控长文本隐写术框架,从而避免了补充培训。该框架利用知识图谱和任务描述中的三元组构建提示,引导大语言模型(LLM)生成与三元组内容一致的文本。随后,该模型通过对围绕采样目标词建立的候选词库进行编码,有效地嵌入了秘密信息。实验结果表明,我们的框架既能确保隐写文本的隐蔽性,又能保持预期内容的相关性和一致性。此外,它还能灵活地适应各种应用场景,展示了其在实际应用中的潜力和优势。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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