Biometric Data Encryption Using a New Five Dimensional Hyper-Chaotic System
Bibliographic record
Abstract
Interest in the biometric data has significantly increased as a result of its potential as one of the reliable methods of authentication.To provide safe storage and transmission of the biometric data images over the public networks, a fast and lossless cryptosystem is highly necessary.The present study introduces a new approach for biometric image encryption with the use of a hyper-chaotic map.A hyper-5D chaotic system has been suggested as a solution for diffusion and confusion problems, with the added advantage of providing a vast key space.This method is heavily dependent upon the chaotic sequences that are produced through the chaotic system in 5-D.A strong level of encryption is ensured with the use of such sequences for the modification and reorganization of pixel values through the image.The proposed system's effectiveness has been evaluated with the use of several performance measures from the security analysis.These included key space analysis, key sensitivity analysis, histogram analysis, correlation coefficient, peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI), information entropy, mean square error (MSE), and time efficiency analysis.The strengths of the suggested cryptosystem against brute force, differential, and statistical attacks have been confirmed by the security analysis findings.
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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 | 0.000 |
| 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 | 0.001 |
| 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".