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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/1233
Title: Recovery based high capacity reversible data hiding scheme using even-odd embedding
Authors: Samayveer Singh
Issue Date: 2018
Publisher: Springer New York LLC
Abstract: There have been discussed various data hiding methods which can embed the secret data in an image. However, the image gets destroyed after extraction of the secret data at the receiving end. Thus, there exists a need to have data hiding methods which can recover the image after extraction of the secret data. Such types of schemes are called reversible data hiding schemes which are commonly used in sensitive military, legal, and medical applications. The existing reversible data hiding schemes either provide good hiding capacity but low quality stego-image or good quality stego-image but poor data hiding capacity because the stego-image quality and the hiding capacity are diametrically related metrics. To address this problem, we propose a high capacity reversible data hiding scheme using recovery strategy. It hides the secret data into a cover image in two phases. In the first phase, the cover image is scanned in a specific scan order andconstructs a location map in which even valued pixels are denoted by ‘1’ and odd valued pixels by ‘0’. It embeds the secret data into every pixel of the image by changing its value at most by 1. The second phase repeats the process of the first phase embedding so that some of the pixels can berecovered to its original form as well as the secret data can further be embedded. Experimentally, it is proved that the proposed scheme provides good quality of stego-image and having the high data hiding capacity at the same time. Further, it is able to maintain the image quality even when the secret data is embedded in layers. © 2017, Springer Science+Business Media, LLC.
URI: https://doi.org/10.1007/s11042-017-5156-1
http://lrcdrs.bennett.edu.in:80/handle/123456789/1233
ISSN: 1380-7501
Appears in Collections:Journal Articles_SCSET

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