Towards robust image watermarking via random distortion assignment based meta-learning

Recently, deep learning-based image watermarking methods have been proposed for copyright protection, which are robust to common post-processing operations. However, they suffer from distinct performance drops to open-set distortions, where distortions applied on testing samples are unseen in the training stage. To address this issue, we propose a random distortion assignment-based meta-learning framework for robust image watermarking, where meta-train and meta-test tasks are constructed to simulate open-set distortion scenarios. The embedding and extraction network of watermark information is constructed based on the invertible neural network and equipped with a multi-stage distortion layer, which can conduct random combinations of basic post-processing operators. Besides, to obtain a better balance between robustness and visual imperceptibility, a hybrid loss function is designed by considering global and local similarities based on wavelet decomposition to capture multi-scale texture information. Extensive experiments are conducted by considering various open-set distortions to verify the superiority of the proposed method.