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Cognitive Computation
Erschienen in: Cognitive Computation 2/2016

01.04.2016

Cognitive Computation of Compressed Sensing for Watermark Signal Measurement

verfasst von: Huimin Zhao, Jinchang Ren

Erschienen in: Cognitive Computation | Ausgabe 2/2016

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Abstract

As an important tool for protecting multimedia contents, scrambling and randomizing of original messages is used in generating digital watermark for satisfying security requirements. Based on the neural perception of high-dimensional data, compressed sensing (CS) is proposed as a new technique in watermarking for improved security and reduced computational complexity. In our proposed methodology, watermark signal is extracted from the CS of the Hadamard measurement matrix. Through construction of the scrambled block Hadamard matrix utilizing a cryptographic key, encrypting the watermark signal in CS domain is achieved without any additional computation required. The extensive experiments have shown that the neural inspired CS mechanism can generate watermark signal of higher security, yet it still maintains a better trade-off between transparency and robustness.
Vorheriger Artikel Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation
Nächster Artikel A Novel Hybrid Text Summarization System for Punjabi Text

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Metadaten
Titel
Cognitive Computation of Compressed Sensing for Watermark Signal Measurement
verfasst von
Huimin Zhao
Jinchang Ren
Publikationsdatum
01.04.2016
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 2/2016
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-015-9357-5

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