Generative Reversible Data Hiding by Image-to-Image Translation via GANs

Abstract

The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of rewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography-based generative adversarial networks, in this paper, a novel generative reversible data hiding (GRDH) scheme by image translation is proposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation model with CycleGAN. After image translation, a stego image with different semantic information will be obtained. The secret message and the original input image can be recovered separately by a well-trained message extractor and the inverse transform of the image translation. The experimental results have verified the effectiveness of the scheme.