This paper introduces a deep learning-based framework for video watermarking that achieves robust, imperceptible, and fast embedding under a wide range of visual and temporal conditions. The proposed method is organized into seven modules that collaboratively perform frame encoding, semantic region analysis, block selection, watermark transformation, and spatiotemporal injection, followed by decoding and multi-objective optimization. A key component of the framework is its ability to learn a visual importance map, which guides a saliency-based block selection strategy. This allows the model to embed the watermark in perceptually redundant regions while minimizing distortion. To enhance resilience, the watermark is distributed across multiple frames, leveraging temporal redundancy to improve recovery under frame loss, insertion, and reordering. Experimental evaluations conducted on a large-scale video dataset demonstrate that the proposed method achieves high fidelity, while preserving low decoding error rates under compression, noise, and temporal distortions. The proposed method operates processing 38 video frames per second on a standard GPU. Additional ablation studies confirm the contribution of each module to the system’s robustness. This framework offers a promising solution for watermarking in streaming, surveillance, and content verification applications.
Cedillo-Hernandez et al. (Mon,) studied this question.