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Two-Dimensional Histogram Modification for Reversible Data Hiding in Partially Encrypted H.264/AVC Videos

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10082))

Abstract

Due to the security and privacy-preserving requirements from cloud computing platforms, it is sometimes desired that the video content is accessible in encrypted form. Reversible data hiding in the encrypted domain has gained increasing attention, as it can perform data hiding in encrypted medias without decryption which preserves the confidentiality of the content. In this paper, an efficient reversible data hiding scheme for encrypted H.264/AVC videos is proposed. During H.264/AVC encoding, the intra-prediction mode (IPM), motion vector difference (MVD), and the sign bits of residue coefficients are encrypted using a standard stream cipher. Then, every two adjacent coefficients are grouped into coefficient pairs. Based on the resulting coefficient pairs, the data-hider may reversibly embed secret data into the encrypted H.264/AVC video by using two-dimensional histogram modification. In addition, dynamic threshold is utilized to determine the embedding region. With an encrypted video containing hidden data, data extraction can be carried out either in encrypted or decrypted domain. Experimental results demonstrate the superiority of the proposed scheme.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61301247, 61672302), Zhejiang Provincial Natural Science Foundation of China (LY17F020013, LZ15F020002), Public Welfare Technology Application Research Project of Zhejiang Province (2015C33237, 2015C31110), National undergraduate innovation and entrepreneurship training programs (201611058002).

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Correspondence to Dawen Xu .

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Xu, D., Zhu, Y., Wang, R., Fu, J., Chen, K. (2017). Two-Dimensional Histogram Modification for Reversible Data Hiding in Partially Encrypted H.264/AVC Videos. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_29

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  • DOI: https://doi.org/10.1007/978-3-319-53465-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53464-0

  • Online ISBN: 978-3-319-53465-7

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