  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 scheme (GRDH) 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. Experimental results have verified the effectiveness of the
scheme.
