Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid
Baowei Wang, Fan Yang, Yufeng Wu, Changyu Dai, Wenjue Huang, Xingyuan Zhao · 2023
Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance.