High-Capacity Image Steganography Algorithm Based on Image Style Transfer

Xinliang Bi,Jia Liu,Chao Wang,Xiaoyuan Yang,Zhili Zhou

doi:10.1155/2021/4179340

Xinliang Bi, Jia Liu + Show 3 more

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https://doi.org/10.1155/2021/4179340

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Abstract

Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

Highlights

While the advancement of social informatization has brought tremendous changes to people’s lifestyles, it has brought some hidden dangers
Traditional image steganography algorithms embed secret information by changing the pixel values of the image and ensure that the embedding has a minimal impact on the cover image by minimizing the loss function [1,2,3]. e biggest difference between these algorithms is the design of the loss function
We use a lot of experiments to verify that the images generated by ISTNet are indistinguishable from other stylized images on the Internet and analyze the influence of different secret images on stego images. e difference between the stego image generated by ISTNet and some other high-capacity steganography algorithms is analyzed, and the steganography capacity is compared with other steganography algorithms based on deep learning

Summary

Introduction

While the advancement of social informatization has brought tremendous changes to people’s lifestyles, it has brought some hidden dangers. Traditional image steganography algorithms embed secret information by changing the pixel values of the image and ensure that the embedding has a minimal impact on the cover image by minimizing the loss function [1,2,3]. Some scholars have proposed a neural network STNet [13] that can embed secret information in the process of image style transfer. Is greatly improves the security of the steganography algorithm On this basis, we proposed ISTNet. We improve the decoder of STNet, fuse the secret image features with the adaptive instance normalization (AdaIN) [14] layer results in multiple scales, and hide a grayscale image during the image style transfer process.

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Experiments

Conclusion