HIWANet: A high imperceptibility watermarking attack network.

Digital image watermarking technology has made a significant contribution to the copyright protection of digital images. In recent years, researchers have focused on designing various watermarking algorithms to enhance resistance against different forms of attacks. However, the evolution of watermarking attack technology has been sluggish, thus impeding possible advancements in digital copyright protection. Existing watermarking attack methods exhibit a notable drawback, causing substantial deterioration in visual quality and undermining the practical utility of attacked images. In this paper, we propose a high imperceptibility watermarking attack network, named HIWANet, based on deep neural networks. To enhance the watermarking attack ability, a feature extraction module (FEM) is aimed to better capture watermark information features, and a watermarking attack module (WAM) is constructed to learn high-level abstract features of the images. In addition, to ensure the imperceptibility of the watermarking attack, an asymmetric loss function is designed to maintain the quality of the attacked watermarked image. In the experiments, we randomly select 2000 color images from the PASCAL VOC2012 database as the dataset, with the training and test sets containing 1000 distinct images. Compared to traditional watermarking attack methods, our HIWANet achieves a significant increase in bit error rate (improved by 242%), indicating a higher attack ability. Meanwhile, it brings more than 26% improvement in the attack imperceptibility. Furthermore, our HIWANet also offers significant advantages compared to deep learning-based watermarking attack methods. [ABSTRACT FROM AUTHOR]