An Improved Image Steganography Framework Based on Y Channel Information for Neural Style Transfer

Wenjie Lin,Yijun Liu,Wujian Ye,Chengmin Liu,Xueke Zhu,Chin-Chen Chang

doi:10.1155/2022/2641615

Wenjie Lin, Yijun Liu + Show 4 more

Open Access

https://doi.org/10.1155/2022/2641615

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Abstract

Neural style transfer has effectively assisted artistic design in recent years, but it has also accelerated the tampering, synthesis, and dissemination of a large number of digital image resources without permission, resulting in a large number of copyright disputes. Image steganography can hide secret information in cover images to realize copyright protection, but the existing methods have poor robustness, which is hard to extract the original secret information from stylized steganographic (stego) images. To solve the above problem, we propose an improved image steganography framework for neural style transfer based on Y channel information and a novel structural loss, composed of an encoder, a style transfer network, and a decoder. By introducing a structural loss to restrain the process of network training, the encoder can embed the gray-scale secret image into Y channel of the cover image and then generate steganographic image, while the decoder can directly extract the above secret image from a stylized stego image output by the style transfer network. The experimental results demonstrate that the proposed method can effectively recover the original secret information from the stylized stego image, and the PSNR of the extracted secret image and the original secret image can reach 23.4 and 27.29 for the gray-scale secret image and binary image with the size of 256×256, respectively, maintaining most of the details and semantics. Therefore, the proposed method can not only preserve most of the secret information embedded in a stego image during the stylization process, but also help to further hide secret information and avoid steganographic attacks to a certain extent due to the stylization of a stego image, thus protecting secret information like copyright.

Highlights

Nowadays, deep learning technology has made remarkable breakthroughs in computer vision, text processing, clustering [1, 2], and voice recognition with its powerful ability of feature learning
To solve the above problem, we propose an improved image steganography framework adapting to neural style transfer by introducing Y channel information and a structural loss
According to [11], the Y channel contains rich semantic information. us, to improve the image quality after embedding secret image Is, we propose to embed the Is into the Y channel of a cover image Ic to obtain a stego image Is+c; the stylized stego image Iss+c is obtained by the style transfer network with the input of Is+c; at last, the hidden secret image Ios is decoded by the decoder network with the input of Iss+c

Summary

Introduction

Deep learning technology has made remarkable breakthroughs in computer vision, text processing, clustering [1, 2], and voice recognition with its powerful ability of feature learning. The generative adaptive network (GAN) model is used to encode data into images with good results These methods have poor robustness and are difficult to extract effective secret information from stylized stego images [4]. To protect and recover the secret information more effectively, we propose an improved image steganography framework adapting to neural style transfer, consisting of an encoder network, a style transfer network, and a decoder network. By introducing the Y channel information and a novel structural loss to restrain the training of decoder and encoder networks, the encoder can effectively embed the gray-scale secret image into the Y channel of the cover image, and the decoder can directly obtain the original secret information from the stylized stego image.

Related Work

The Improved Image Steganography Framework for Neural Style Transfer

The Experimental Analysis

Evaluation indexes

Conclusion and Future Works