Bibliographic record
Abstract
Digital audio scrambling is a process used in audio security applications. Scrambling of audio files breaks the correlation between adjacent samples in order to convert the original audio to an unintelligible format. Scrambling is used to protect the audio against wiretapping and illegal surveillance, in addition to being a step in security algorithms, such as watermarking and encryption algorithms. Cellular automata are models that are discrete in nature and depend on simple and local rules to achieve an interesting overall behavior. Two-dimensional cellular automata were previously proposed as a key generation mechanism to scramble audio files. The mechanism was built upon be researchers in the multimedia security field. This paper explores the use of one-dimensional cellular automata in audio scrambling, which simplifies the process as deploying two-dimensional cellular automata requires changing the dimension of the audio file and the one-dimensional cellular automata does not, additionally, elementary one-dimensional cellular automata requires less parameters to configure. The scrambling degree is used to evaluate the model effectiveness in breaking the correlation of adjacent samples. In the experiments, different parameters are taken into account including the cellular automata class, the iterations needed and the method used to calculate the cells at the boundary. Experiments show that the one-dimensional cellular automata are capable of scrambling the audio file without any dimensional change and the chaotic rules tested give the highest scrambling degree.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".