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Data Embedding in Scrambled Video by Rotating Motion Vectors
Ahmed, Afaf Eltayeb Mohamedelbagir Shanableh, Tamer
Ahmed, Afaf Eltayeb Mohamedelbagir
Shanableh, Tamer
Date
2022-03
Advisor
Type
Article
Peer-Reviewed
Postprint
Peer-Reviewed
Postprint
Degree
Description
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.