AI-Based Digital Watermarking for Video: Design, Training Strategies, and Engineering Deployment
The paper presents an AI‑driven invisible video watermarking system that combines a convolutional encoder/decoder with SE blocks, a simulated‑JPEG degradation layer, multi‑term loss, block‑wise processing, anchor‑based alignment and redundancy voting, achieving high visual fidelity and robust recovery after double‑compression in large‑scale platforms like Bilibili.