  Digital contents have grown dramatically in recent years, leading to
increased attention to copyright. Image watermarking has been considered one of
the most popular methods for copyright protection. With the recent advancements
in applying deep neural networks in image processing, these networks have also
been used in image watermarking. Robustness and imperceptibility are two
challenging features of watermarking methods that the trade-off between them
should be satisfied. In this paper, we propose to use an end-to-end network for
watermarking. We use a convolutional neural network (CNN) to control the
embedding strength based on the image content. Dynamic embedding helps the
network to have the lowest effect on the visual quality of the watermarked
image. Different image processing attacks are simulated as a network layer to
improve the robustness of the model. Our method is a blind watermarking
approach that replicates the watermark string to create a matrix of the same
size as the input image. Instead of diffusing the watermark data into the input
image, we inject the data into the feature space and force the network to do
this in regions that increase the robustness against various attacks.
Experimental results show the superiority of the proposed method in terms of
imperceptibility and robustness compared to the state-of-the-art algorithms.
