Low Complexity Multimedia Encryption
Bibliographic record
Abstract
Selective encryption algorithms have been proposed to encrypt syntax elements such as intra prediction modes, the sign bit of nonzero DCT coefficients, along with the sign bit of motion vectors.These syntax elements are sensitive enough to produce effective scrambling effect with a relative low computational cost.In this paper, a novel scheme is proposed to further optimize the computational overhead incurred by the encryption for energy critical multimedia applications.The proposed scheme adjusts the selection of syntax elements to be encrypted according to the scene transitions within adjacent video frames.The ratio of intra-coded macroblocks in inter (P and B) frames is calculated and compared with an adaptive threshold value to detect the scene transitions.Furthermore, based on statistical analysis for a few video sequences, a dynamic threshold model to detect the scene transition is proposed.When there is a scene transition between the previous video frame and the current video frame, intra prediction modes and the sign bit of DCT coefficients in the current frame are chosen as syntax elements to be encrypted, whereas in the absence of a scene transition, the sign bit of motion vectors is chosen as the only sensitive syntax elements to be encrypted.Experimental results show that compared with previous work in this field, the proposed scheme can efficiently lower the computational cost incurred by the encryption while maintaining a similar perceptual scrambling effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".