DITW: A High-Performance Deep-Independent Template-Based Watermarking

Yaokun Fang, Changxi Huang, Chengxin Zhao, Hefei Ling, Xunjie Lin, Jinlong Guo

Abstract: Watermarking algorithms based on deep Convolutional Neural Networks (CNN) have been extensively studied and shown to effectively improve performance. Most deep watermarking algorithms are dependent on the participation of host images, which results in more time and computing resources for embedding watermarks. In order to reduce the complexity of watermark embedding while retaining the performance optimization brought by CNN, we propose a high-performance Deep-Independent Template-based Watermarking (DITW). The proposed method generates deep templated-watermarks based on secret messages independently, without the involvement of host images. Then the embedding process is implemented through additive operation, which greatly improves the efficiency of embedding watermarks. To improve the performance of the network, We design the pixel-change loss and a learnable Embedding Strength (ES) Matrix Adaptor which replaces the universal ES. Through extensive experiments, we demonstrate that our scheme outperforms the existing state-of-the-art deep template-based watermarking algorithms in terms of imperceptibility and capacity without robustness degradation.