Energy-aware encryption for securing video transmission in internet of multimedia things
Abstract
High Efficiency Video Coding (HEVC) encryption, which has been proposed to encrypt intra prediction modes (structural information), transform coefficients (texture information), and motion related codewords (motion information), has received considerable attention recently. However, there is still the issue of efficiency when HEVC encryption is applied in the Internet of Multimedia Things (IoMT). Aiming at this challenge, in this paper, we propose a new low-overhead HEVC encryption scheme for energy-constrained IoMT. Concretely, the proposed scheme adjusts the selection of the aforementioned syntax elements to be encrypted according to the structure, texture, and motion energy present in each frame. It works as follows. The energy levels of quantized coefficients and motion vectors are calculated and compared with adaptive threshold values to classify the energy level in each video frame. When there is a high energy frame in the video, all the syntax elements are encrypted. When there is a low energy frame, alternate syntax elements are encrypted for achieving low encryption overhead. Moreover, in the case of transform coefficients, to withstand the interpolation attack, alternate coefficients are encrypted after correlating the frame with its neighboring coefficients. Extensive experiments were conducted, and the results demonstrate that the proposed scheme efficiently reduces the encryption overhead with low impact on the security level, making it suitable for IoMT.
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Traditional video streams are mainly transmitted and stored in a plaintext model, which leads to the risk of data leakage.
“Traditional video streams are mainly transmitted and stored in a plaintext model, which leads to the risk of data leakage [31,32].”
From Secure Video Surveillance Framework in Smart City (2021) · cites this paper as [31]
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Secure Video Surveillance Framework in Smart City
Li, H., Xiezhang, T., Yang, C., Deng, L., & Yi, P. (2021) · Sensors · doi:10.3390/s21134419 · Cited by 31 · see citing works
- Authors
- Thiyagarajan, K., Lu, R., El-Sankary, K., & Zhu, H.
- Affiliations
- Canadian Nuclear Laboratories (CA)
- University of New Brunswick (CA)
- Xidian University (CN)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2019-03
- Volume
- 29
- Issue
- 3
- Pages
- 610-624
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2791302671
- Cited by
- 40
- References
- 32
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 10 · 2021: 7 · 2020: 6 · 2019: 3
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Discovery (Rgpin 04009)
- NBIF Start-Up (Rif 2017-012)
- National Natural Science Foundation of China (61672411)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Keywords
- video transmission security, Internet of Multimedia Things, adaptive threshold, motion vectors, intra prediction modes
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