Robust Invisible Video Watermarking with Attention

TL;DR

This paper introduces RivaGAN, an attention-based deep learning architecture for robust video watermarking that ensures minimal visual distortion and resilience against common video modifications.

Contribution

The paper presents RivaGAN, a novel attention-driven deep learning model with adversarial training for improved robustness and minimal distortion in video watermarking.

Findings

Achieves state-of-the-art robustness in deep learning-based video watermarking.

Produces watermarked videos with minimal visual distortion.

Demonstrates resilience against common video processing operations.

Abstract

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.