Enhancement of the Generation Quality of Generative Linguistic Steganographic Texts by a Character-Based Diffusion Embedding Algorithm (CDEA)
Yingquan Chen, Qianmu Li, Aniruddha Bhattacharjya, Xiaocong Wu, Huifeng Li, Qing Chang, Le Zhu, Xiao Yan · Applied Sciences · 2025
Generative linguistic steganography aims to produce texts that remain both perceptually and statistically imperceptible. The existing embedding algorithms often suffer from imbalanced candidate selection, where high-probability words are overlooked and low-probability words dominate, leading to reduced coherence and fluency. We introduce a character-based diffusion embedding algorithm (CDEA) that uniquely leverages character-level statistics and a power-law-inspired grouping strategy to better balance candidate word selection. Unlike prior methods, the proposed CDEA explicitly prioritizes high-probability candidates, thereby improving both semantic consistency and text naturalness. When combined with XLNet, it effectively generates longer sensitive sequences while preserving quality. The experimental results showed that CDEA not only produces steganographic texts with higher imperceptibility and fluency but also achieves stronger resistance to steganalysis compared with the existing approaches. Future work will be to enhance statistical imperceptibility, integrate CDEA with larger language models such as GPT-5, and extend applications to cross-lingual, multimodal, and practical IoT or blockchain communication scenarios.