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<p>Image steganography is a process of embedding a secret image into a cover image to achieve secret transmission and accurately recovering a secret image from a stego image. To further investigate the high invisibility and extraction accuracy of steganography, this study presents RISRANet, a reversible image steganography network utilizing residual structure and mixed attention mechanism, markedly enhancing both the fidelity of stego image and the accuracy of restoring secret image. The network uses INN as the overall framework, adopts a double-branch structure, extracts deep features using the mixed attention mechanism, and employs channel shuffle to promote information interaction between different features. This paper introduces dilated convolution to design a multi-scale convolution attention module that combines feature information from different scales, highlights essential features, and precisely locates the ideal embedding position. In addition, the residual structure is constructed to allow for feature reuse, and the structural similarity is introduced into the loss function to improve the accuracy of information recovery. The experimental results suggest that the framework achieves secure hiding and lossless extraction of secret images, superior to comparative algorithms in multiple evaluation metrics.</p>

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Reversible image steganography based on residual structure and attention mechanism

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月3日

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  • Lianshan Liu,
  • Shanshan Tong,
  • Qianwen Xue

摘要

Image steganography is a process of embedding a secret image into a cover image to achieve secret transmission and accurately recovering a secret image from a stego image. To further investigate the high invisibility and extraction accuracy of steganography, this study presents RISRANet, a reversible image steganography network utilizing residual structure and mixed attention mechanism, markedly enhancing both the fidelity of stego image and the accuracy of restoring secret image. The network uses INN as the overall framework, adopts a double-branch structure, extracts deep features using the mixed attention mechanism, and employs channel shuffle to promote information interaction between different features. This paper introduces dilated convolution to design a multi-scale convolution attention module that combines feature information from different scales, highlights essential features, and precisely locates the ideal embedding position. In addition, the residual structure is constructed to allow for feature reuse, and the structural similarity is introduced into the loss function to improve the accuracy of information recovery. The experimental results suggest that the framework achieves secure hiding and lossless extraction of secret images, superior to comparative algorithms in multiple evaluation metrics.