A Six-Dimensional Hyperchaotic Pseudorandom Sequence for Enhanced Voice Encryption
Bibliographic record
Abstract
Over recent decades, the demand for robust voice encryption algorithms has escalated to fortify the security of speech transmission over vulnerable channels such as the internet. Among the myriad of available methodologies, those underpinned by chaos theory have garnered significant attention due to their inherent pseudorandomness, acute sensitivity to initial conditions, and control parameters. These attributes render them capable of encrypting a variety of data types, encompassing but not limited to videos, images, and audio. This study presents a novel voice encryption approach predicated on a sixdimensional (6D) hyperchaotic system. In the proposed method, six unique keys are generated from the 6D hyperchaotic system. The initial three keys are employed to permute the human voice signal, while the subsequent trio is engaged in the diffusion process. The efficacy of this scheme is evaluated on several parameters: Mean Square Error (MSE), Signal-To-Noise Ratio (SNR), correlation coefficient, Peak Signal-To-Noise Ratio (PSNR), key sensitivity, key space, and entropy analysis. The Libri-Speech dataset serves as the test bench for the proposed system. The key space has been determined to be 2465. The system's performance is notable, with correlation coefficients ranging between -0.00276 and 0.002759, entropy values from 14.74399 to 14.74942, PSNR values from 4.2814 to 4.7875, SNR values from -30.3854 to -9.2364, and a nearly zero MSE range of 0.3321 to 0.3731 between original and extracted signals. This study underscores the potential of the 6D hyperchaotic system in enhancing information security, specifically for voice encryption. The findings may pave the way for more secure communication protocols in an increasingly interconnected digital world.
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) | 0.001 | 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".