Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel
Existing linguistic steganography schemes often overlook the conditional probability (CP) of tokens in the candidate pool, allocating the one coding to all tokens, which results in identical selection likelihoods. This approach leads to the selection of low-CP tokens, degrading the quality of stegos and making them more detectable. This paper proposes a scheme based on the interval allocated, called DAIRstega. DAIRstega first uses a portion of the read secret to build the roulette area. Then, this scheme uses the idea of the roulette wheel and takes the CPs of tokens as the main basis for allocating the roulette area (i.e., the interval length). Thus, tokens with larger CPs are allocated more area. The secret will have an increased likelihood of selecting a token with a higher CP. During allocation, we designed some allocation functions and three constraints to optimize the process. Additionally, DAIRstega supports prompt-based controllable generation of stegos. Rich experiments show that the proposed embedding way and DAIRstega perform better than the existing ways and baselines, which shows strong perceptual, statistical, and semantic concealment, as well as anti-steganalysis ability. It can also generate high-quality longer stegos, addressing the deficiencies in this task. DAIRstega is confirmed to have potential as a secure watermarking, offering insights for its development.
Code (1)
Tasks
Language ModellingLarge Language ModelLinguistic steganographySteganalysisSimilar Papers 제목 키워드 기반
A high-capacity linguistic steganography based on entropy-driven rank-token mapping
Linguistic steganography enables covert communication through embedding secret messages into innocuous texts; however, current methods face critical limitations in payload capacity and security. Traditional modification-…
Zero-shot Generative Linguistic Steganography
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 stegote…
In-Context LearningLinguistic steganographyFADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis
The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. However, detection is severely hampered by two fundamental issues during model training. Firstly, …
Provably Secure Generative Linguistic Steganography
Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext). However, previous methods generally lead …
Language ModelingLanguage ModellingLinguistic steganographyA Secure and Disambiguating Approach for Generative Linguistic Steganography
Segmentation ambiguity in generative linguistic steganography could induce decoding errors. One existing disambiguating way is removing the tokens whose mapping words are the prefixes of others in each candidate pool. Ho…
Linguistic steganographySegmentationSteganalysis