Genetic Algorithm Using Feistel and Genetic Operator Acting at the Bit Level for Images Encryption
Bibliographic record
Abstract
In this paper, a new medical image encryption technique based on genetic algorithms acting at the bit level will be developed.Initially, a transformation to a binary matrix notation of the original image is applied, followed by an evaluation function determined by the Hamming distance between the obtained image and another pseudo-random image generated from chaotic maps used.This discrimination function divides the image, viewed as a population where each row represents an individual, into two categories: a strong population and a weak population.An enhanced Feistel round will be implemented by introducing a chaotic mating between the two categories based on a circular shift for the right bloc and a pseudo-random permutation for the left bloc.Next, a genetic crossover adapted for image encryption will be performed with another pseudo-random vector under the control of a crossover table.To ensure the robustness of our approach, a genetic mutation will be applied at the end of the encryption.A multitude of images of different sizes and formats have been tested using our approach, with encouraging results.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".