A Robust Generative Image Steganography Method based on Guidance Features in Image Synthesis
Youqiang Sun, Jianyi Liu, Ru Zhang · 2023
The existing generative steganography methods have the limitations of the low capacity and poor stego quality. The target image which are served as guidance features and used to translate the image from the original to special one during the synthetic process. These guidance features are abundant, stable and do not contain the identity information which can be used as cover in steganography. This paper proposed a generative image steganography by using the guidance features in image synthesis, and a secret fusion algorithm is proposed to solve the problems of guidance features embedding and extraction errors. Due to the robustness of styles and attributes, the embedded guidance features can be extracted directly in receiver side from the synthesized image without code book or database. Compared with the existing generative steganography methods, the proposed method can achieve a higher security and quality while maintaining a larger embedding capacity.