Abstract:Although traditional watermarking attack methods can distorb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a novel imperceptible watermarking attack method based on residual learning is proposed. Firstly, a watermarking attack model based on convolutional neural network is constructed to carry out the end-to-end nonlinear learning betwwen watermarking image and no-watermarked image, which can model a mapping from watermarking images to no-watermarked images for achieving the purpose of watermark attack. Secondly, a suitable number of feature extraction block is selected according to the embedding region of watermak information to extract the feature map containing watermak information. As watermarking image and no-watermarked image are largely similar, the learning ability of watermarking attack model is limited in the training process, which makes it difficult for the model to reach the convergence state. The residual learning mechanism is introduced to improve the convergence speed and learning ability of watermarking attack model. The imperceptibility of attacked watermakerd image can be improved by reducing the difference between residual image (the subtraction operation between watermarked image and feature map) and no-watermarked iamge. In addition, the image dataset for training watermarking attack model is constructed according to DIV2K2017 super-resolution dataset and the attacked robust color image watermarking algorithm based on quaternion exponential moment. The experimental results show the watermarking attack model can attack the robust watermarking algorithm with high BER (Bit Error Rate) on the premise of ensuring high imperceptibility of watermarked images.