Coverless information hiding based on Generative Model
This addresses the need for secure image transmission resistant to steganalysis, offering a novel approach compared to traditional steganography, though it appears incremental in applying generative models to this domain.
The paper tackles the problem of secure image transmission by proposing a coverless information hiding method using generative models, which generates a normal-looking image from a secret image for transmission and then reconstructs the original at the receiver, achieving high capacity, safety, and reliability as shown in experiments.
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.