Abstract 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.