Selective Encryption of VVC Encoded Video Streams for the Internet of Video Things
Why this work is in the frame
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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).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it