An encrypted coverless information hiding method based on generative models

Invisibility is an important target for image steganography; however, the traditional image steganography methods inevitably leave traces of their modifications in cover images due to the process used to embed secret messages. In this paper, we propose an encrypted coverless information hiding method that transfers secret images between two different image domains using generative models. Our method includes two stages: encryption and decryption. In the encryption stage, we first embed a secret image into a public image (one domain) to obtain a synthetic image; then, we utilize that image as the input to the first generative model F to obtain an encrypted image (another domain). Adversarial loss and an extraction module are added to improve the quality of the encrypted images generated in this stage. In the decryption stage, we design a second generative model G to reconstruct the synthetic images from the encrypted images. Finally, the secret image is separated from the reconstructed synthetic image. In our extensive experiments, we adopt the images in the MNIST dataset as the secret images, which allows us to use the recognition rate as a measure of the secret image reconstruction quality. The experimental results indicate that our method is not only able to generate quality encrypted images compared with current popular image-to-image translation methods but also possesses greater security, robustness and reconstruction ability. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces an encrypted coverless information hiding method that transfers secret images between different image domains using generative models, addressing the issue of traditional image steganography methods leaving traces in cover images. Experimental results show that the method is able to generate high-quality encrypted images with strong security and reconstruction ability.