Energy-Aware Encryption for Securing Video Transmission in Internet of Multimedia Things
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".