Abstract
Image hiding aims to secretly embed secret information into a cover image and then recover the hidden data with minimal or no loss at the receiving end. Many works on steganography and deep learning have proved the huge prospects of deep learning in the field of image information hiding. However, current deep learning-based steganography research exposes significant limits, among which key issues such as how to improve embedding capacity, imperceptibility, and robustness remain crucial for image-hiding tasks. This article introduces PUA-Net, a new end-to-end neural network model for image steganography. PUA-Net consists of three main components: 1) the CbDw attention module, 2) the attention gate module, and 3) the partial combination convolution module. Each of these components utilizes structural reparameterization operations. In addition, we propose a residual image minimization loss function and use a combination of loss functions based on this loss function. This model can seamlessly embed bit stream information of different capacities into images to generate stego images that are imperceptible to the human eye. Experimental results confirm the effectiveness of our model, achieving an RS-BPP of 5.98 when decoding the extracted secret information and recovering the cover image. When only the extracted secret information is output, the model achieves a maximum RS-BPP of 6.94. Finally, experimental results show that our PUA-Net model outperforms deep learning-based steganography approaches on COCO, ImageNet, and BOSSbase datasets, including GAN-based methods such as Stegastamp and SteganoGAN.
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The data that support the findings of this study are available from the author, Feng Lin, upon reasonable request.
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Funding
This work was supported by major programs incubation plan of Xizang Minzu University: 22MDZ03; Key project of the Natural Science Foundation of the Tibet Autonomous Region: image forgery detection and location based on multi semantics and attention: XZ202301ZR0042G; The National Natural Science Foundation of China (No.62262062).
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Feng Lin conceptualized the main methods and wrote the article. Ruxue, as the corresponding author of this article, provided theoretical guidance on methods and guidance on article writing. Shi Dong designed experimental methods. Fuhao Ding visualized the experimental results. Yixin Han organized the experimental data.
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Lin, F., Xue, R., Dong, S. et al. PUA-Net: end-to-end information hiding network based on structural re-parameterization. Appl Intell 55, 142 (2025). https://doi.org/10.1007/s10489-024-06081-x
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DOI: https://doi.org/10.1007/s10489-024-06081-x