A blind watermarking system based on deep learning model

Lichao Zhang, Wangbin Li, Hailiang Ye

Abstract: In recent years, as an important means of digital image copyright protection, image invisible watermarking technology has attracted more and more attention. Based on the watermarking system built by deep learning model, this paper proposes an optimization algorithm which can extract watermark and improve robustness. The watermark is embedded and extracted by the encoder and decoder of the model respectively. In order to make the model robust to various image attacks, two noise layers are added to the model. In order to further improve the robustness and concealment of watermark, adversarial training is used. In order to improve the accuracy of model joint training, a two-step training strategy is adopted. The first step is to train the best pre training model of encoder and decoder. The second step is to train different decoders according to different image attack means, so as to improve the robustness of the whole watermarking system. Experimental results show that, compared with other watermarking algorithms based on deep learning model, the proposed method is superior to other algorithms in the concealment and robustness.