Staged Adaptive Blind Watermarking Scheme.

In traditional digital image watermarking methods, the stren-gth factor is calculated from the content of the carrier image, which can find a balance between the robustness and imperceptibility of encoded images. However, traditional methods do not consider the feature of the message and it is also unrealistic to calculate the strength factor of each image separately when faced with a huge number of images. In recent years, digital image watermarking methods based on deep learning have also introduced the strength factor. They assign the strength factor of each image to a fixed value to better adjust the robustness and imperceptibility of the image. We hope that the network can choose the most appropriate strength factor for each image to achieve a better balance. Therefore, we propose a staged adaptive blind watermarking scheme. We designed a new component - the adaptor, and used two stages of training by training different components in different stages, and improved the robustness and imperceptibility of watermarked images. By comparing the experimental results, our algorithmic scheme shows better results compared to current advanced algorithms.