Selective Encryption of VVC Encoded Video Streams for the Internet of Video Things
Bibliographic record
Abstract
Visual sensors serve as a critical component of the Internet of Things (IoT). There is an ever-increasing demand for broad applications and higher resolutions of videos and cameras in smart homes and smart cities, such as in security cameras. To utilize this large volume of video data generated from networks of visual sensors for various machine vision applications, it needs to be compressed and securely transmitted over the Internet. H.266/VVC, as the new compression standard, brings the highest compression for visual data. To provide security along with high compression, a selective encryption method for hiding information of videos is presented for this new compression standard. Selective encryption methods can lower the computation overhead of the encryption while keeping the video bitstream format which is useful when the video goes into untrusted blocks such as transcoding or watermarking. Syntax elements that represent considerable information are selected for the encryption, i.e., luma Intra Prediction Modes (IPMs), Motion Vector Difference (MVD), and residual signs., then the results of the proposed method are investigated in terms of visual security and bit rate change. Our experiments show that the encrypted videos provide higher visual security compared to other similar works in previous standards, and integration of the presented encryption scheme into the VVC encoder has little impact on the bit rate efficiency (results in 2% to 3% bit rate increase).
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 | 0.000 |
| 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".