ReMark: Receptive Field based Spatial WaterMark Embedding Optimization
  using Deep Network
Natan Semyonov Rami Puzis Asaf Shabtai Gilad Katz
Watermarking is one of the most important copyright protection tools for
digital media. The most challenging type of watermarking is the imperceptible
one, which embeds identifying information in the data while retaining the
latter's original quality. To fulfill its purpose, watermarks need to withstand
various distortions whose goal is to damage their integrity. In this study, we
investigate a novel deep learning-based architecture for embedding
imperceptible watermarks. The key insight guiding our architecture design is
the need to correlate the dimensions of our watermarks with the sizes of
receptive fields (RF) of modules of our architecture. This adaptation makes our
watermarks more robust, while also enabling us to generate them in a way that
better maintains image quality. Extensive evaluations on a wide variety of
distortions show that the proposed method is robust against most common
distortions on watermarks including collusive distortion.