增强隐写文本提取：评估 NLP 模型对准确率和语义连贯性的影响
Enhancing Steganographic Text Extraction: Evaluating the Impact of NLP Models on Accuracy and Semantic Coherence
AI总结：
针对传统 LSB 隐写在复杂字符编码下提取不准的问题，提出融合 NLP 大模型纠错、语义分析与信息重建的 LSB-NLP 框架，显著提升汉字隐写文本提取准确率与鲁棒性。
摘要：
本研究讨论了一种将图像隐写技术与 Natural Language Processing（NLP，自然语言处理）大模型相结合的新方法，旨在提高隐写文本提取的准确率和鲁棒性。传统的 Least Significant Bit（LSB，最低有效位）隐写技术在处理汉字等复杂字符编码时，信息提取的准确率和鲁棒性面临挑战。为解决这一问题，本研究提出了创新的 LSB-NLP 混合框架。该框架整合了 NLP 大模型的先进能力，如错误检测、纠错和语义一致性分析，以及信息重建技术，从而显著增强隐写文本提取的鲁棒性。实验结果表明，LSB-NLP 混合框架在提高隐写文本提取准确率方面表现出色，尤其是在处理汉字时。本研究结果不仅证实了将图像隐写技术与 NLP 大模型相结合的有效性，也为信息隐藏领域的研究与应用提出了新思路。这一跨学科方法的成功实施，展示了将图像隐写技术与自然语言处理技术相融合在解决复杂信息处理问题方面的巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
