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- W3157516770 abstract "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." @default.
- W3157516770 created "2021-05-10" @default.
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- W3157516770 date "2021-04-26" @default.
- W3157516770 modified "2023-10-14" @default.
- W3157516770 title "DVMark: A Deep Multiscale Framework for Video Watermarking" @default.
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- W3157516770 doi "https://doi.org/10.48550/arxiv.2104.12734" @default.
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