  Due to the rapid growth of machine learning tools and specifically deep
networks in various computer vision and image processing areas, application of
Convolutional Neural Networks for watermarking have recently emerged. In this
paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark)
which can be adapted for any desired transform space. The framework is composed
of two Fully Convolutional Neural Networks with the residual structure for
embedding and extraction. The whole deep network is trained end-to-end to
conduct a blind secure watermarking. The framework is customizable for the
level of robustness vs. imperceptibility. It is also adjustable for the
trade-off between capacity and robustness. The proposed framework simulates
various attacks as a differentiable network layer to facilitate end-to-end
training. For JPEG attack, a differentiable approximation is utilized, which
drastically improves the watermarking robustness to this attack. Another
important characteristic of the proposed framework, which leads to improved
security and robustness, is its capability to diffuse watermark information
among a relatively wide area of the image. Comparative results versus recent
state-of-the-art researches highlight the superiority of the proposed framework
in terms of imperceptibility and robustness.
