Video Invisible Watermarking at Scale

This post introduces the implementation of invisible watermarking at scale for video content. It details the challenges and solutions for embedding imperceptible signals for content provenance, including detecting AI-generated videos, verifying upload order, and identifying creation tools. A key contribution is the development of a CPU-based watermarking solution that achieves comparable performance to GPUs with improved operational efficiency, by optimizing threading and addressing data transfer, inference latency, and model loading bottlenecks. The post also highlights the importance of managing BD-Rate impact and addressing visual quality regressions through custom post-processing and manual inspections, noting the insufficiency of traditional quality metrics.