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

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.