  The goal of video watermarking is to embed a message within a video file in a
way such that it minimally impacts the viewing experience but can be recovered
even if the video is redistributed and modified, allowing media producers to
assert ownership over their content. This paper presents RivaGAN, a novel
architecture for robust video watermarking which features a custom
attention-based mechanism for embedding arbitrary data as well as two
independent adversarial networks which critique the video quality and optimize
for robustness. Using this technique, we are able to achieve state-of-the-art
results in deep learning-based video watermarking and produce watermarked
videos which have minimal visual distortion and are robust against common video
processing operations.
