基于深度学习的图像隐写方法，因存在模型参数量和计算量大等问题，而面临高参数和计算负载的挑战，为此提出了一种轻量高效的图像隐写方法。首先在编码器和解码器中引入Ghost模块，降低了编码器和解码器的参数量和计算量。其次提出了一个多尺度特征融合模块，用以捕捉多维数据中的复杂关系。最后提出了一个新颖的混合损失函数，可在保持模型不变的情况下提升图像隐写质量。实验结果表明，所提方法在256×256像素的图像上峰值信噪比达到47.59 dB。与目前最优的图像隐写方法相比，所提方法的隐写质量提升1.7 dB，参数量减少77%，计算量减少91%，在隐写质量上有较优的表现，同时模型的参数量和计算量大大降低，实现了模型的轻量高效化。
Deep learning-based image steganography often faces challenges due to the large number of model parameters and high computational demands. In response, a lightweight and efficient image steganography method is proposed. Firstly Ghost module is introduced into the encoder and decoder, which reduces the number of parameters and computational complexity. Furthermore, a multi-scale feature fusion module is designed to capture complex relationships within multidimensional data. In addition, a novel hybrid loss function is proposed, which can improve image steganography quality without modifying the model. Experimental results show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 47.59 dB on a 256×256 pixel image. Compared with the current best image steganography method, the steganography quality is improved by 1.7 dB, the number of parameters is reduced by 77%, and number of computations by 91%. These results confirm that the proposed method effectively enhances steganography quality with reduced number of parameters and computational complexity of the model, achieving a lightweight and efficient model.
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