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.

翻译：本研究探讨了一种融合图像隐写技术与自然语言处理（NLP）大模型的新方法，旨在提升隐写文本提取的准确性与鲁棒性。传统最低有效位（LSB）隐写技术在处理复杂字符编码（如汉字）时，面临信息提取准确性与鲁棒性的挑战。针对这一问题，本文提出了一种创新的LSB-NLP混合框架。该框架整合了NLP大模型的先进能力，包括错误检测与纠正、语义一致性分析及信息重构技术，从而显著增强了隐写文本提取的鲁棒性。实验结果表明，LSB-NLP混合框架在提升隐写文本提取准确性方面表现优异，尤其在处理汉字时效果显著。本研究不仅验证了图像隐写技术与NLP大模型结合的有效性，还为信息隐藏领域的研究与应用提出了新思路。这种跨学科方法的成功实施，展示了图像隐写技术与自然语言处理技术在解决复杂信息处理问题中的巨大潜力。