Bibliographic record
Abstract
The increased popularity of multimedia applications places a great demand on efficient data storage and transmission techniques. Network communication, especially over a wireless network, can easily be intercepted and must be protected from eavesdroppers. Unfortunately, encryption and decryption are slow, and it is often difficult, if not impossible, to carry out real-time secure image and video communication and processing. Methods have been proposed to combine compression and encryption together to reduce the overall processing time, but they are either insecure or too computationally intensive. We propose a novel solution called partial encryption, in which a secure encryption algorithm is used to encrypt only part of the compressed data. Partial encryption is applied to several image and video compression algorithms in this paper. Only 13-27% of the output from quadtree compression algorithms is encrypted for typical images, and less than 2% is encrypted for 512/spl times/512 images compressed by the set partitioning in hierarchical trees (SPIHT) algorithm. The results are similar for video compression, resulting in a significant reduction in encryption and decryption time. The proposed partial encryption schemes are fast, secure, and do not reduce the compression performance of the underlying compression algorithm.
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) | 0.003 | 0.001 |
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".