A Novel High-Capacity Information Hiding Scheme Based on Improved U-Net

Xiaoli Wang,Lianshan Liu,Yanjun Peng,Lingzhuang Meng,Weimin Zheng

doi:10.1155/2022/4345494

Xiaoli Wang, Lianshan Liu + Show 3 more

Open Access

https://doi.org/10.1155/2022/4345494

Copy DOI

Abstract

With the gradual introduction of deep learning into the field of information hiding, the capacity of information hiding has been greatly improved. Therefore, a solution with a higher capacity and a good visual effect had become the current research goal. A novel high-capacity information hiding scheme based on improved U-Net was proposed in this paper, which combined improved U-Net network and multiscale image analysis to carry out high-capacity information hiding. The proposed improved U-Net structure had a smaller network scale and could be used in both information hiding and information extraction. In the information hiding network, the secret image was decomposed into wavelet components through wavelet transform, and the wavelet components were hidden into image. In the extraction network, the features of the hidden image were extracted into four components, and the extracted secret image was obtained. Both the hiding network and the extraction network of this scheme used the improved U-Net structure, which preserved the details of the carrier image and the secret image to the greatest extent. The simulation experiment had shown that the capacity of this scheme was greatly improved than that of the traditional scheme, and the visual effect was good. And compared with the existing similar solution, the network size has been reduced by nearly 60%, and the processing speed has been increased by 20%. The image effect after hiding the information was improved, and the PSNR between the secret image and the extracted image was improved by 6.3 dB.

Highlights

With the advent of the current network information age, information security has received more and more attention
Traditional information hiding used the correlation between digital image pixels to hide, which could be divided into spatial domain schemes and transform domain schemes. e spatial domain scheme directly utilized the correlation between pixels [2], and more researched schemes included least significant bit (LSB), histogram translation [3], difference expansion [4], and other methods. e transform domain scheme used a series of transforms to convert image into transform coefficients and used the correlation between the coefficients for information hiding
Its secret image was randomly selected in the dataset and converted into a 256 × 256 grayscale image. e hardware used in the experiment was GPU Tesla P100, the software environment was pytho3.6 and pytorch, and the initialization parameters during training were the initial network learning rate lr 0.001 and the number of iterations epoch 1000

Summary

Introduction

With the advent of the current network information age, information security has received more and more attention. Wu et al [10] used deep convolutional networks for image steganography Such a scheme was not a simple LSB change, and the residual image did not contain identifiable secret images, so the effect of the scheme was good. GAN network performed well in the field of image generation, so it had good visual effects when generating images containing secret information. E scheme hid the secret information after DWT transformation into the image through a deep learning network. A novel high-capacity information hiding scheme based on improved U-Net was proposed by combined U-Net network and wavelet transform. The secret image was converted into four wavelet components by the wavelet transform, and the wavelet components were hidden into carrier image through the improved U-Net network.

Preliminaries

Proposed Scheme

A HVD 64

Conclusion