2016
DOI: 10.1109/tcsvt.2015.2416591
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Secure Reversible Image Data Hiding Over Encrypted Domain via Key Modulation

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Cited by 187 publications
(102 citation statements)
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“…The decoder side by further exploiting the spatial correlation using a dissimilar estimation equation and side match technique. For both methods in [10] and [11], decrypting image and extracting data must be jointly executed. Recently, Zhou et al [12] proposed a novel RDH-EI method for joint decryption and extraction, in which the correlation of plaintexts is further exploited by distinguishing the encrypted and non-encrypted pixel blocks with a twoclass SVM classifier.…”
Section: IImentioning
confidence: 99%
“…The decoder side by further exploiting the spatial correlation using a dissimilar estimation equation and side match technique. For both methods in [10] and [11], decrypting image and extracting data must be jointly executed. Recently, Zhou et al [12] proposed a novel RDH-EI method for joint decryption and extraction, in which the correlation of plaintexts is further exploited by distinguishing the encrypted and non-encrypted pixel blocks with a twoclass SVM classifier.…”
Section: IImentioning
confidence: 99%
“…Therefore, it seems that the proposed algorithm performed better than the previous BFI algorithm and it is efficient as well as scalable for practical applications. J. Zhou, W. Sun et al [9], proposed a novel reversible image data hiding method (RIDH). In this paper two class SVM classifier is designed to separate out encrypted and nonencrypted patches of images.…”
Section: Related Workmentioning
confidence: 99%
“…The proposed algorithm estimates the optimal marginal distribution which is faster than the BFI algorithm. A novel reversible image data hiding method (RIDH) is proposed in [9]. Two class SVM classifier is demonstrated to separate encrypted and non-encrypted patches of images, it gives higher embedding capacity and it also able to reconstruct original image and embedded message.…”
Section: Related Workmentioning
confidence: 99%