Enhancing Steganographic Text Extraction: Evaluating the Impact of NLP
  Models on Accuracy and Semantic Coherence
Mingyang Li Maoqin Yuan Luyao Li Han Pengsihua
This study discusses a new method combining image steganography technology
with Natural Language Processing (NLP) large models, aimed at improving the
accuracy and robustness of extracting steganographic text. Traditional Least
Significant Bit (LSB) steganography techniques face challenges in accuracy and
robustness of information extraction when dealing with complex character
encoding, such as Chinese characters. To address this issue, this study
proposes an innovative LSB-NLP hybrid framework. This framework integrates the
advanced capabilities of NLP large models, such as error detection, correction,
and semantic consistency analysis, as well as information reconstruction
techniques, thereby significantly enhancing the robustness of steganographic
text extraction. Experimental results show that the LSB-NLP hybrid framework
excels in improving the extraction accuracy of steganographic text, especially
in handling Chinese characters. The findings of this study not only confirm the
effectiveness of combining image steganography technology and NLP large models
but also propose new ideas for research and application in the field of
information hiding. The successful implementation of this interdisciplinary
approach demonstrates the great potential of integrating image steganography
technology with natural language processing technology in solving complex
information processing problems.