Genetic Algorithm Using Feistel and Genetic Operator Acting at the Bit Level for Images Encryption
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
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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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 | 0.000 | 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 | 0.000 |
| 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