Enhancing steganographic text extraction: evaluating the impact of NLP models on accuracy and semantic coherence

Mingyang Li; Maoqin Yuan; Luyao Li; Han Pengsihua

doi:10.1117/12.3031035

19 July 2024 Enhancing steganographic text extraction: evaluating the impact of NLP models on accuracy and semantic coherence

Mingyang Li, Maoqin Yuan, Luyao Li, Han Pengsihua

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810U (2024) https://doi.org/10.1117/12.3031035
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingyang Li, Maoqin Yuan, Luyao Li, and Han Pengsihua "Enhancing steganographic text extraction: evaluating the impact of NLP models on accuracy and semantic coherence", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810U (19 July 2024); https://doi.org/10.1117/12.3031035

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KEYWORDS

Steganography

Image processing

Image quality

Data modeling

Semantics

Binary data

Data hiding

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