Abstract
Data embedding in videos has several important applications including Digital Rights Management, preserving confidentiality of content, authentication and tampering detection. This paper proposes a novel data embedding solution in scrambled videos by rotating motion vectors of predicted macroblocks. The rotation of motion vectors and the propagation of motion compensation error serve another purpose, which is video scrambling. A compliant decoder uses machine learning to counter-rotate the motion vectors and extract embedded message bits. To achieve this, the decoder uses a sequence-dependent approach to train a classifier to distinguish between macroblocks reconstructed using rotated and un-rotated motion vectors. In the testing phase, motion vectors belonging to a classified macroblock are compared against the reviewed rotated motion vectors and the message bits are extracted. Furthermore, to guarantee accurate classification at the decoder, a constrained encoding approach is proposed in which data embedding is restricted to motion vectors that can be correctly counter-rotated at the decoder. The proposed solution is referred to as Classifying Rotated Vectors or CRVs for short. Experimental results revealed that scrambled videos can be reconstructed correctly without quality loss with a bitrate increase at the encoder of around 6% and an average data embedding rate of 1.68 bits per MB.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27–53
Correa G, Assuncao P, Agostini L, da Silva Cruz L (2015) Fast HEVC encoding decisions using data mining,” IEEE Transactions on Circuits and Systems for Video Technology, 25(4)
Guan B, Xu D, Li Q (2020) An efficient commutative encryption and data hiding scheme for HEVC video. IEEE Access, An Efficient Commutative Encryption and Data Hiding Scheme for HEVC Video
Hassan M, Shanableh T (2018) Predicting split decisions of coding units in HEVC video compression using machine learning techniques. Multimedia Tools and Applications, Springer, https://doi.org/10.1007/s11042-018-6882-8
Hong W, Chen T, Wu H (2012) An improved reversible data hiding in encrypted images using side match. IEEE Signal Process. Letters, 19(4)
Livingston F (2005) Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper
Ma K, Zhang W, Zhao X, Yu N, Li F (2013) Reversible data hiding in encrypted images by reserving room before encryption. IEEE transactions on information forensics and security, 8(3)
Ma Z, Jiang M, Gao H, Wang Z (2018) Blockchain for digital rights management. Future Generation Computer Systems 89
Ong S, Wong K, Tanaka K (2015) Scrambling–embedding for JPEG compressed image. Signal Processing 109
Patel R, Lad K, Patel M, Desai M (2021) A hybrid DST-SBPNRM approach for compressed video steganography. Multimedia Systems 27:417–428. https://doi.org/10.1007/s00530-020-00735-9
Peixoto E, Shanableh T, Izquierdo E (2014) H.264/AVC to HEVC video transcoder based on dynamic thresholding and content modeling. IEEE Transactions on Circuits and Systems for Video Technology, 24(1)
Shanableh T (2012) Data hiding in MPEG video files using multivariate regression and flexible macroblock ordering, IEEE transactions on information forensics and security, 7(2), April
Shanableh T (2012) Matrix encoding for data hiding using multilayer video coding and transcoding solutions. Signal Processing: Image Communication, Elsevier, 27(9)
Shanableh T (2017) Altering Split decisions of coding units for message embedding in HEVC. Multimedia and applications, Springer, https://doi.org/10.1007/s11042-017-4787-6
Su P, Tsai T, Chien Y (2017) Partial frame content scrambling in H.264/AVC by information hiding. Multimed Tools Appl 76(5):7473–7496
Toh K, Tran Q, Srinivasan D (2004) Benchmarking a reduced multivariate polynomial pattern classifier. IEEE Trans Pattern Anal Mach Intell 26(6):740–755
Y. Wang, M. O’Neill, F. Kurugollu, E. O’Sullivan (2015) Privacy region protection for H.264/AVC with enhanced scrambling effect and a low bitrate overhead. Signal Processing: Image Communication 35
Xu D (2019) Commutative encryption and data hiding in HEVC video compression. IEEE Access 7
Xu D, Wang R (2015) Context adaptive binary arithmetic coding-based data hiding in partially encrypted H.264/AVC videos. Journal of Electronic Imaging 24(3)
D. Xu, R. Wang, Y. Shi (2014) Data hiding in encrypted H.264/AVC video streams by codeword substitution. IEEE Transactions on Information Forensics and Security, 9 (4)
Xu D, Wang R, Zhu Y (2017) Tunable data hiding in partially encrypted H.264/AVC videos. Journal of Visual Communication and Image Representation 45
Yao Y, Zhang W, Yu N (2016) Inter-frame distortion drift analysis for reversible data hiding in encrypted H.264/AVC video bitstreams. Signal Processing 128
Acknowledgements
The work in this paper was supported by a faculty research Grant from the American University of Sharjah (FRG19-S-E102). This paper represents the opinions of the authors and does not mean to represent the position or opinions of the American University of Sharjah
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Eltayeb, A., Shanableh, T. Data embedding in scrambled video by rotating motion vectors. Multimed Tools Appl 81, 25473–25496 (2022). https://doi.org/10.1007/s11042-022-12945-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12945-6