PUA-Net: end-to-end information hiding network based on structural re-parameterization

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.