Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80617
Title: When Compressive Sensing Meets Data Hiding
Authors: Hua, Guang
Xiang, Yong
Bi, Guoan
Keywords: Compressed sensing
Data models
Multimedia communication
Privacy
Transforms
Watermarking
Issue Date: 2016
Source: Hua, G., Xiang, Y., & Bi, G. (2016). When Compressive Sensing Meets Data Hiding. IEEE Signal Processing Letters, 23(4), 473-477.
Series/Report no.: IEEE Signal Processing Letters
Abstract: We present a novel framework of performing multimedia data hiding using an over-complete dictionary, which brings compressive sensing to the application of data hiding. Unlike the conventional orthonormal full-space dictionary, the over-complete dictionary produces an underdetermined system with infinite transform results. We first discuss the minimum norm formulation (ℓ2-norm) which yields a closed-form solution and the concept of watermark projection, so that higher embedding capacity and an additional privacy preserving feature can be obtained. Furthermore, we study the sparse formulation (ℓ2-norm) and illustrate that as long as the ℓ0-norm of the sparse representation of the host signal is less than the signal's dimension in the original domain, an informed sparse domain data hiding system can be established by modifying the coefficients of the atoms that have not participated in representing the host signal. A single support modification-based data hiding system is then proposed and analyzed as an example. Several potential research directions are discussed for further studies. More generally, apart from the ℓ2- and ℓ0-norm constraints, other conditions for reliable detection performance are worth of future investigation.
URI: https://hdl.handle.net/10356/80617
http://hdl.handle.net/10220/40555
ISSN: 1070-9908
DOI: 10.1109/LSP.2016.2536110
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2016.2536110].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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