A robust VVC video encryption scheme based on the dynamical chaotification model
Year: 2023
Type: article
Abstract: With the considerable surge of video assets, the demand for video content security has undergone substantial growth. Since the advent of H.264/AVC and H.265/HEVC selective encryption algorithms (SEAs) have become a focus. However, there is little research based on the H.266/VVC. To fill the gap, a VVC encryption scheme is proposed in this paper. Considering that most existing SEAs are easily cracked under cryptographic attacks, a dynamical chaotification model (DCM) is designed to resist chosen-plaintext attack (CPA). Additionally, given that a majority of existing SEAs are incapable of decrypting correctly in the case of packet loss, each slice is encrypted independently to enhance robustness. Firstly, the three-dimensional (3D) DCM is designed based on the exponential feedback and the boundary function. Secondly, the dynamical parameter in the 3D-DCM is updated by the encoding parameters in the VVC encoder. Then the initial values and other model parameters of the 3D-DCM are scrambled by the hash values of the slice header (SHs). Finally, the texture, motion, and structure information of VVC videos are encrypted using the iterative output of the 3D-DCM. In particular, three exclusive elements in H.266/VVC are selected in the RSES, including matrix-based intra prediction (MIP), cross-component linear model prediction (CCLM), and multiple transform selection (MTS). Experimental results demonstrate the outstanding performance of the proposed RSES in robustness and resistance to CPA, while gratifying bit rate and real-time requirements. Therefore, it is a promising encryption scheme in cryptographic applications.
Authors: Chen Chen, Xingjun Wang, Jinghan Xu
Institutions: Tsinghua University, Heilongjiang Communications Polytechnic
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,350
Awards: G2175934847, G2195155527, G6025887399
OpenAlex ID: W4387004577
