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<p>To improve the imperceptibility of image steganography, an image steganography method based on a conditional invertible neural network is proposed in this paper. First, we design a conditional invertible neural network to obtain high-quality stego images with rich high-level semantic information and clear spatial details. On the basis of the conditional directivity of the conditional invertible neural network, we can adjust the semantic information of the stego image accurately and ensure the controllability of the stego image content. We introduce a dual cross-attention module into the network structure. The integration of dual cross-attention modules enhances feature extraction and captures complex image details to improve steganographic accuracy. In addition, the introduction of the convolutional block attention module in the convolutional layer directs the model's focus to key image regions, refining stego image quality. We increase the number of convolutional blocks, which improves the ability of feature extraction and reuse. Many experiments are carried out on datasets. For the cover and stego image pairs, the PSNR value reached 43.62&#xa0;dB, and for the secret and recovery image pairs, the PSNR value reached 46.48&#xa0;dB. The experimental results show that the image quality and imperceptibility of this method are better than those of other state-of-the-art image steganography methods.</p>

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Research on image steganography based on a conditional invertible neural network

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Menghua Liang,
  • Hongtu Zhao

摘要

To improve the imperceptibility of image steganography, an image steganography method based on a conditional invertible neural network is proposed in this paper. First, we design a conditional invertible neural network to obtain high-quality stego images with rich high-level semantic information and clear spatial details. On the basis of the conditional directivity of the conditional invertible neural network, we can adjust the semantic information of the stego image accurately and ensure the controllability of the stego image content. We introduce a dual cross-attention module into the network structure. The integration of dual cross-attention modules enhances feature extraction and captures complex image details to improve steganographic accuracy. In addition, the introduction of the convolutional block attention module in the convolutional layer directs the model's focus to key image regions, refining stego image quality. We increase the number of convolutional blocks, which improves the ability of feature extraction and reuse. Many experiments are carried out on datasets. For the cover and stego image pairs, the PSNR value reached 43.62 dB, and for the secret and recovery image pairs, the PSNR value reached 46.48 dB. The experimental results show that the image quality and imperceptibility of this method are better than those of other state-of-the-art image steganography methods.