Hybrid encryption and Riesz-based biometric authentication: a novel approach for secure greyscale image transmission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a novel greyscale image encryption and authentication mechanism by combining hybrid encryption with the Riesz transform for the first time. The system employs asymmetric encryption and optoelectronic implementation, integrating key techniques such as spiral phase mask, unequal modulus, random modulus decomposition and the QZS algorithm to enhance key space and enable authentication. The system's resilience is demonstrated through numerical simulations in MATLAB environment. Excellent statistical measures for decrypted image are obtained from the proposed approach: a mean squared error (MSE) of 4.9436×10−18, a peak signal-to-noise ratio (PSNR) of 221.19 dB and a perfect correlation coefficient (CC) of 1. Additionally, with number of pixel change rate (NPCR) of 99.94% and unified average changing intensity (UACI) of 33.348, the system exhibits strong resilience against differential attacks. The high entropy value of 7.9954 confirms strong randomness and security, reinforcing the system’s resilience against differential attacks. Despite offering a high degree of security and accuracy the system maintains an efficient total encryption time of 2.37 seconds, including both encryption and authentication procedures, these findings establish the proposed system as a robust solution for secure image transmission and storage in today’s data-sensitive environment. This work represents a significant advancement in biometric-based image encryption by integrating novel hybrid transforms with Riesz-based authentication.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it