Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret image of size 256 x 256 is first decomposed using an eight-level Haar wavelet transform. The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands, which are then embedded into the carrier image via a hiding network. During the recovery process, the container image is divided into four non-overlapping parts, each employed to reconstruct a low-resolution secret image. These lowresolution secret images are combined using dense modules to obtain a high-quality secret image. The experimental results showed that even under destructive attacks on the container image, the proposed algorithm is successful in recovering a high-quality secret image, indicating that the algorithm exhibits a high degree of robustness against various attacks. The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain, making it suitable for practical applications. In conclusion, the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms. Its ability to recover high-quality secret images even in the presence of destructive attacks makes it an attractive option for various applications. Further research and experimentation can explore the algorithm's performance under different scenarios and expand its potential applications.