Generative Reversible Data Hiding by Image to Image Translation via GANs

05/08/2019
by   Zhuo Zhang, et al.
0

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.

READ FULL TEXT
research
05/08/2019

Somewhat Reversible Data Hiding by Image to Image Translation

The traditional reversible data hiding technique is based on image modif...
research
02/07/2019

Reversible GANs for Memory-efficient Image-to-Image Translation

The Pix2pix and CycleGAN losses have vastly improved the qualitative and...
research
11/26/2021

TRIP: Refining Image-to-Image Translation via Rival Preferences

Relative attribute (RA), referring to the preference over two images on ...
research
02/14/2018

Recursive Chaining of Reversible Image-to-image Translators For Face Aging

This paper addresses the modeling and simulation of progressive changes ...
research
10/24/2019

Guided Image-to-Image Translation with Bi-Directional Feature Transformation

We address the problem of guided image-to-image translation where we tra...
research
11/21/2016

Image-to-Image Translation with Conditional Adversarial Networks

We investigate conditional adversarial networks as a general-purpose sol...
research
05/27/2021

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

This work is the first to employ and adapt the image-to-image translatio...

Please sign up or login with your details

Forgot password? Click here to reset