Hybrid Vigenère-Hill Approach for Color Image Encryption
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces an enhanced encryption scheme for color images, combining improved Vigen re and Hill cipher techniques.Our approach leverages two carefully selected chaotic maps, exploiting their extreme sensitivity to initial conditions for cryptographic security.The encryption process begins with RGB channel separation and vector conversion, followed by initial confusion operations generating a partially encrypted image vector.This vector is then divided into 3-pixel subblocks for subsequent processing.Each block undergoes multi-stage encryption controlled by a binary vector, employing three expanded substitution tables with optimized confusion-diffusion functions.These functions operate sequentially across pixels with chaining mechanisms between adjacent pixels.The modified Hill cipher then processes each block using an invertible matrix combined with dynamic translation vectors, effectively addressing the linearity limitations of traditional Hill cipher implementations.To enhance security, we implement an inter-block diffusion mechanism that dynamically links each block's final encrypted pixel with the next block's initial pixel through a specialized diffusion function.This design significantly strengthens avalanche effects while providing robust resistance against differential attacks.Tests on a diverse set of randomly chosen color images yielded statistical (histogram, correlation, entropy) and differential (UACI, NPCR) metrics meeting international standards, confirming our cryptosystem's robustness against known attacks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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