Two-Stage Watermark Removal Framework for Spread Spectrum Watermarking

Spread spectrum (SS) watermarking has gained significant attention as it prevents attackers from reading, tampering with, or removing watermarks. Secret key estimation can help with the first two unauthorized operations but cannot remove watermarks. Moreover, existing deep-learning watermark removal methods do not consider the characteristics of SS watermarking, thus leading to unsatisfactory results. In this paper, we design a secret key estimation method that treats secret key estimation as a binary classification problem and updates the estimated key via backpropagation and parameter optimization algorithms. We develop a watermark removal network using quaternion convolutional neural networks (QCNNs) to learn watermark features while capturing the relationship between channels to improve image quality. Based on our estimation method and QCNN-based network, we propose a two-stage watermark removal framework that utilizes information of the secret key to train the network. A loss function is introduced to directly prevent watermark extraction, thereby improving removal performance. Extensive experiments demonstrate the superiority of our methods over the state-of-the-art methods.