RAN: A randomness-anchored watermark attacking network with stealth and effectiveness

Research on watermark attacking is essential to reinforce robust watermarking methods by providing new attacking benchmarks. Recently, there is an emergence of attacking methods based on deep learning, in which perceptual loss and watermark loss are utilized to train the neural networks for the imperceptibility of watermarked images and the disruption of attacked watermarks. In this work, we propose a novel randomness-anchored attacking network (RAN) based on deep learning. In RAN, we introduce an alternative watermark loss to attack the watermarks into random noises by anchoring to randomness rather than the original watermarks. Extensive attacking experiments of comparisons with attacking schemes show that the proposed RAN models can achieve satisfying performance in preserving the visual fidelity of attacked watermarked images with competitive attacking ability. The proposed methods add new inspirations to the design of stealthy and effective attacking models based on deep learning, with significant implications for developing robust watermarking. The source code and data is shared at https://github.com/kq409/Watermark-Attack.