Generative Reversible Data Hiding by Image to Image Translation via GANs
This work addresses steganography security for data hiding applications, but it appears incremental as it builds on existing GAN-based methods.
The paper tackles the problem of reversible data hiding by proposing a generative scheme using GANs to create stego images via image translation, avoiding traces from cover modification, and demonstrates effectiveness through experimental results.
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.