  Video watermarking embeds a message into a cover video in an imperceptible
manner, which can be retrieved even if the video undergoes certain
modifications or distortions. Traditional watermarking methods are often
manually designed for particular types of distortions and thus cannot
simultaneously handle a broad spectrum of distortions. To this end, we propose
a robust deep learning-based solution for video watermarking that is end-to-end
trainable. Our model consists of a novel multiscale design where the watermarks
are distributed across multiple spatial-temporal scales. It gains robustness
against various distortions through a differentiable distortion layer, whereas
non-differentiable distortions, such as popular video compression standards,
are modeled by a differentiable proxy. Extensive evaluations on a wide variety
of distortions show that our method outperforms traditional video watermarking
methods as well as deep image watermarking models by a large margin. We further
demonstrate the practicality of our method on a realistic video-editing
application.
