An adaptive encryption based genetic algorithms for medical images
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel efficient symmetric encryption technique that can be applied to medical images. It uses genetic algorithm which makes it highly adaptive. Standard DI-COM images are segmented into a number of regions based on pixel intensity and entropy measurements. The novelty of the selective encryption method lies in the use of several encryption algorithms with variable key lengths to control the processing time required for the encryption process and the robustness quality. Encryption processing time, robustness of the encrypted image and the side information required for transmission of the decryption key are the main parameters for optimization. The trade-off among them stems from the variation in processing time with the key length of encryption algorithm, image size, number of regions and the side information to reduce processing time while maintaining a high level of robustness.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it