基于跨模态学习的鲁棒文本隐写

doi:10.3969/j.issn.0255-8297.2025.03.004

Abstract

Abstract: This paper proposes a robust text steganography method based on cross-modal learning, embedding secret information by generating sentences consistent with the semantics of image. To improve text generation quality, both semantic and regional features of the image are integrated. Moreover, a random word deletion attack layer is designed during training to further enhance the robustness of the steganographic texts. Experiments evaluate the model’s robustness against both text and image attack. The results demonstrate that the proposed method achieves superior text generation quality and robustness, effectively improving the cognitive concealment of steganographic text.

Key words: steganography, generative text steganography, cross-modal, robustness

CLC Number:

TP309

MA Ting, TAN Yun, QIN Jiaohua, XIANG Xuyu. Robust Text Steganography Based on Cross-Modal Learning[J]. Journal of Applied Sciences, 2025, 43(3): 403-414.

References

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Robust Text Steganography Based on Cross-Modal Learning

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