Enhancing Robustness and Imperceptibility of Blind Watermarking with Improved Message Processor

Abstract

The current state-of-the-art(SOTA) blind watermark embedding method MBRS based on deep learning is less robust to Crop, and additional diffusion layers need to be added for optimization. However, the diffusion layer will make the model less robust to noise other than Crop. Therefore, MBRS which needs to add or delete components is not a practical watermarking framework. Not only that, MBRS is easy to generate chessboard artifacts, resulting in the generated watermark being easy to be detected by the human eye. Therefore, we construct a more generalized watermarking framework and propose an improved blind watermarking method. The method addresses the shortcomings of MBRS by using an improved message processor with sub-pixel convolution layers and low-frequency features and incorporating double discriminators to improve the performance of the network. Extensive experiments show that our method demonstrates superior results compared to the SOTA method.