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Chengfu Ou

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AAAI Conference 2026 Conference Paper

A Content-Preserving Secure Linguistic Steganography

Lingyun Xiang
Chengfu Ou
Xu He
Zhongliang Yang
Yuling Liu

Existing linguistic steganography methods primarily rely on content transformations to conceal secret messages. However, they often cause subtle yet looking-innocent deviations between normal and stego texts, posing potential security risks in real-world applications. To address this challenge, we propose a content-preserving linguistic steganography paradigm for perfectly secure covert communication without modifying the cover text. Based on this paradigm, we introduce CLstega (Content-preserving Linguistic steganography), a novel method that embeds secret messages through controllable distribution transformation. CLstega first applies an augmented masking strategy to locate and mask embedding positions, where MLM (masked language model)-predicted probability distributions are easily adjustable for transformation. Subsequently, a dynamic distribution steganographic coding strategy is designed to encode secret messages by deriving target distributions from the original probability distributions. To achieve this transformation, CLstega elaborately selects target words for embedding positions as labels to construct a masked sentence dataset, which is used to fine-tune the original MLM, producing a target MLM capable of directly extracting secret messages from the cover text. This approach ensures perfect security of secret messages while fully preserving the integrity of the original cover text. Experimental results demonstrate that CLstega can achieve a 100% extraction success rate, and outperforms existing methods in security, effectively balancing embedding capacity and security.

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AAAI Conference 2025 Conference Paper

Promising Multi-Granularity Linguistic Steganography by Jointing Syntactic and Lexical Manipulations

Chengfu Ou
Lingyun Xiang
Yangfan Liu

Existing modification-based linguistic steganography methods primarily perform linguistic manipulations within a single embedding space to conceal secret information. However, these methods are stringently constrained by the original semantics of the cover text, making it struggle to achieve a satisfactory embedding capacity in a single embedding space. In this paper, we propose a novel Multi-granularity Modification-based Linguistic Steganography framework (MMLS) that hides secret information in both syntactic space and symbolic space, enhancing syntactic naturalness and semantic coherence while further increasing embedding capacity. Specifically, MMLS utilizes a paraphrase generation model to automatically modify the syntactic structure of the given original sentence, which enables the generation of paraphrases and the preservation of semantics simultaneously. Moreover, MMLS employs a distance-aware syntactic bins coding strategy to embed part of secret information into the syntactic space. This strategy utilizes a cluster-based way to partition the implicit syntactic space into a finite number of separate zones, thus increasing the number of candidate paraphrases and avoiding the selection of semantically distorted steganographic texts. Finally, the pre-trained BERT is used to replace some words in candidate paraphrases with their synonyms. Such a design embeds the remaining secret information into symbolic space while ensuring syntactic and semantic naturalness. Experimental results demonstrate that MMLS significantly outperforms existing methods in terms of semantic coherence, embedding capacity, and security.

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