A Novel Two-stage Separable Deep Learning Framework for Practical Blind Watermarking

As a vital copyright protection technology, blind watermarking based on deep learning with an end-to-end encoder-decoder architecture has been recently proposed. Although the one-stage end-to-end training (OET) facilitates the joint learning of encoder and decoder, the noise attack must be simulated in a differentiable way, which is not always applicable in practice. In addition, OET often encounters the problems of converging slowly and tends to degrade the quality of watermarked images under noise attack. In order to address the above problems and improve the practicability and robustness of algorithms, this paper proposes a novel two-stage separable deep learning (TSDL) framework for practical blind watermarking. Precisely, the TSDL framework is composed of noise-free end-to-end adversary training (FEAT) and noise-aware decoder-only training (ADOT). A redundant multi-layer feature encoding network is developed in FEAT to obtain the encoder, while ADOT is used to get the decoder which is robust and practical enough to accept any type of noise. Extensive experiments demonstrate that the proposed framework not only exhibits better stability, greater performance and faster convergence speed compared with current state-of-the-art OET methods, but is also able to resist high-intensity noises that have not been tested in previous works.