Generative high-capacity image hiding based on residual CNN in wavelet domain

Image hiding is the process of hiding a secret image in another meaningful image or other carriers so that the secret image remains imperceptible and can be recovered securely at the receiving end. The output image of an image hiding algorithm hides the secret image and visually appears to be the same as the carrier image, thus reducing the possibility of being attacked. The current hiding algorithms have relatively low hiding capacity and weak security. In this paper, we propose a generative image hiding algorithm based on a residual convolutional neural network (ResCNN) in wavelet domain to overcome the above-mentioned shortcomings. First, the secret image was subjected to wavelet transform. The low-frequency band of wavelet coefficients were discarded, and only the high-frequency bands were retained as features. These features were then effectively embedded into the carrier image by a generative ResCNN. The recovery network was trained simultaneously with the hiding network so as to extract the hidden features from the container and reconstruct the secret image. Pixel shuffle was used to recover a high-resolution secret image. The experimental results show that the proposed image hiding algorithm is capable of obtaining state-of-the-art results in terms of high hiding capacity and strong security measures. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a generative image hiding algorithm based on residual convolutional neural network (ResCNN) in the wavelet domain to overcome the low hiding capacity and weak security measures of current hiding algorithms. The proposed algorithm effectively embeds high-frequency features of a secret image into a carrier image, leading to state-of-the-art results in terms of hiding capacity and security.