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Due to the rapid growth of machine learning tools and specifically deepnetworks in various computer vision and image processing areas, application ofConvolutional Neural Networks for watermarking have recently emerged. In thispaper, we propose a deep end-to-end diffusion watermarking framework (ReDMark)which can be adapted for any desired transform space. The framework is composedof two Fully Convolutional Neural Networks with the residual structure forembedding and extraction. The whole deep network is trained end-to-end toconduct a blind secure watermarking. The framework is customizable for thelevel of robustness vs. imperceptibility. It is also adjustable for thetrade-off between capacity and robustness. The proposed framework simulatesvarious attacks as a differentiable network layer to facilitate end-to-endtraining. For JPEG attack, a differentiable approximation is utilized, whichdrastically improves the watermarking robustness to this attack. Anotherimportant characteristic of the proposed framework, which leads to improvedsecurity and robustness, is its capability to diffuse watermark informationamong a relatively wide area of the image. Comparative results versus recentstate-of-the-art researches highlight the superiority of the proposed frameworkin terms of imperceptibility and robustness.

ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks