DWW: Robust Deep Wavelet-Domain Watermarking With Enhanced Frequency Mask

Shiyuan Tang, Jiangqun Ni, Wenkang Su, Yulin Zhang

Abstract: This letter concentrates on the challenges of deep learning-based robust image watermarking against print-scanning, print-camera, and screen-shooting attacks for “physical channel transmission”. Given the excellent performance demonstrated by wavelet domain watermarking, in this paper, we incorporate the wavelet integrated convolutional neural networks (CNNs) and propose a Deep Wavelet-domain Watermarking (DWW) model, which is dedicated to embedding watermarks in the wavelet domain rather than the spatial domain of the previous arts. In addition, a frequency-domain enhanced mask loss is developed to increase the loss weight in the high-frequency regions of the image during back-propagation, thereby encouraging the model to embed the message in low-frequency components with priority so as to improve the robustness performance. Experiment results show that the proposed DWW consistently outperforms other state-of-the-art (SOTA) schemes by a clear margin in terms of embedding capacity, imperceptibility, and robustness.