3,661 research outputs found
Reversible Data Hiding in Encrypted Images Using MSBs Integration and Histogram Modification
This paper presents a reversible data hiding in encrypted image that employs
based notions of the RDH in plain-image schemes including histogram
modification and prediction-error computation. In the proposed method, original
image may be encrypted by desire encryption algorithm. Most significant bit
(MSB) of encrypted pixels are integrated to vacate room for embedding data
bits. Integrated ones will be more resistant against failure of reconstruction
if they are modified for embedding data bits. At the recipient, we employ
chess-board predictor for lossless reconstruction of the original image by the
aim of prediction-error analysis. Comparing to existent RDHEI algorithms, not
only we propose a separable method to extract data bits, but also content-owner
may attain a perfect reconstruction of the original image without having data
hider key. Experimental results confirm that the proposed algorithm outperforms
state of the art ones
An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images
International audienceReversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryp-tion key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allow us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors and high capacity reversible data hiding approach with embedded prediction errors. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity
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