Coverless information hiding based on Generative Model

TL;DR

This paper introduces a novel coverless image hiding method using generative models, transmitting a normal image that can regenerate the secret image at the receiver, enhancing security and resisting steganalysis.

Contribution

It is the first to propose coverless image hiding based on generative models, avoiding embedded information and improving security against steganalysis.

Findings

High capacity for secret data transmission

Effective resistance to steganalysis tools

Reliable image regeneration at receiver

Abstract

A new coverless image information hiding method based on generative model is proposed, we feed the secret image to the generative model database, and generate a meaning-normal and independent image different from the secret image, then, the generated image is transmitted to the receiver and is fed to the generative model database to generate another image visually the same as the secret image. So we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information hiding method based on generative model, compared with the traditional image steganography, the transmitted image does not embed any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our method has high capacity, safety and reliability.