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Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining
Joint Authors
Wei, Jianguo
Li, Shuo
Song, Zhanjie
Lu, Wenhuan
Sun, Daniel
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
The privacy is a major concern in big data mining approach.
In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining.
In the framework, the watermark is the compressed version of the original speech.
The watermark is embedded into the least significant bit (LSB) layers.
At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech.
To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter.
This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations.
Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method.
Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen.
Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly.
This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.
American Psychological Association (APA)
Li, Shuo& Song, Zhanjie& Lu, Wenhuan& Sun, Daniel& Wei, Jianguo. 2017. Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1202890
Modern Language Association (MLA)
Li, Shuo…[et al.]. Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining. Security and Communication Networks No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1202890
American Medical Association (AMA)
Li, Shuo& Song, Zhanjie& Lu, Wenhuan& Sun, Daniel& Wei, Jianguo. Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1202890
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202890