A Scalable Architecture for Uncompressed-Domain Watermarked Videos

Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However,...

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Bibliographic Details
Published inIEEE transactions on information forensics and security Vol. 14; no. 6; pp. 1432 - 1444
Main Authors Mareen, Hannes, De Praeter, Johan, Van Wallendael, Glenn, Lambert, Peter
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users. Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders. The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos. As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder. Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly. Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder. Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2018.2879301