基于字符识别的无载体隐写

doi:10.3969/j.issn.0255-8297.2025.04.010

摘要/Abstract

摘要： 为提高无载体隐写的隐藏容量，注意到半构造式方法的特点，本文采用英汉谚语翻译构造原则，结合深度学习方法，提出并实现了一种利用汉字字符小图标作为构造元素的无载体隐写方法。构建汉字字符小图标载体库，设计小图标与二进制流之间一一映射的关系。在发送端将输入的秘密消息按照12 bits进行分组，从载体库中寻找对应的汉字小图标拼接成含密载体图像。在接收端，对含密载体图像进行分割，利用深度学习方法识别载体图像中的汉字，根据汉字与二进制流之间的映射关系，实现秘密消息的提取。此外，为了提高该方案的鲁棒性，引入了数据增强方法人工合成文本图像数据集。实验和测试结果表明，与同类无载体隐写方法相比，该方法大大提升了隐藏容量，并且具有良好的鲁棒性。

关键词: 无载体隐写, 半构造式, 隐藏容量, 深度学习

Abstract: In order to enhance the hiding capacity of coverless steganography, this paper proposes and implements a coverless steganography method that uses small icons of Chinese characters as construction elements. Inspired by the semi-constructive approach and the principle of English-Chinese proverb translation, the method integrates a deep learning framework to achieve effective information hiding. Firstly, a carrier library of Chinese character small icons is constructed, and a one-to-one mapping relationship between small icons and binary streams is designed. At the sender, the input secret messages are grouped by 12 bits, and the corresponding small icons of Chinese characters are found from the carrier library and stitched into the secret carrier image. At the receiver, the secret carrier image is first segmented, and the Chinese characters in the carrier image are recognized using deep learning method. The secret message is extracted according to the mapping relationship between the Chinese characters and the binary stream. In addition, in order to improve the robustness of the scheme, a data augmentation strategy is introduced to synthesize text image datasets manually. Experimental results demonstrate that, compared to existing coverless steganography methods, the proposed method significantly improves hiding capacity while maintaining strong robustness.

Key words: coverless steganography, semi-constructive, hiding capacity, deep learning

中图分类号:

TP309

鲁桢, 吴建斌. 基于字符识别的无载体隐写[J]. 应用科学学报, 2025, 43(4): 684-693.

LU Zhen, WU Jianbin. Coverless Steganography Based on Character Recognition[J]. Journal of Applied Sciences, 2025, 43(4): 684-693.

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