Hybrid Vigenère-Hill Approach for Color Image Encryption
Bibliographic record
Abstract
This paper introduces an enhanced encryption scheme for color images, combining improved Vigenè 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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".