Abstract
As an emerging technology, reversible data hiding in encrypted images will be useful and popular in cloud computing due to its ability to preserve the confidentiality. In this paper, a novel framework of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into non-overlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data-hider, who does not know the original image content, may reversibly embed secret data into image difference using a modified version of histogram shifting technique. Data extraction is completely separable from image decryption, i.e., data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, real reversibility is realized, that is, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61301247, 61170137, and 61300055), Public Welfare Technology Application Research Project of Zhejiang Province (2015C33237), Zhejiang Provincial Natural Science Foundation of China (LY13F020013, LZ15F020002), Ningbo Natural Science Foundation (2013A610059), Open Fund of Zhejiang Provincial Top Key Discipline (xkxl1405), Zhejiang Science and Technology Innovation Activities Project Foundation of College Students (2015R424015), and the Opening Project of Shanghai Key Laboratory of Integrate Administration Technologies for Information Security (AGK2013004).
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Xu, D., Chen, K., Wang, R., Su, S. (2016). Completely Separable Reversible Data Hiding in Encrypted Images. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_30
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DOI: https://doi.org/10.1007/978-3-319-31960-5_30
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