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