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

• Published in: IEEE signal processing letters Vol. 31; pp. 3074 - 3078
• Main Authors: Tang, Shiyuan, Ni, Jiangqun, Su, Wenkang, Zhang, Yulin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Back propagation networks; Decoding; Deep Wavelet-domain watermarking (DWW); Discrete wavelet transforms; Embedding; Frequency-domain analysis; Image enhancement; Image quality; Machine learning; Robust image watermarking; Robustness; Training; Visualization; Watermarking; Wavelet domain; wavelet integrated CNNs
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2024.3490399

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.