Partial Encryption Scheme of Medical Images Based on DWT, Secret Image Sharing and Hyperchaotic System
Bibliographic record
Abstract
There has been a significant increase in the demand for secure image storage in healthcare organizations in recent years.Encryption is used to address the challenge of encrypting sizeable digital image files, as full encryption can be computationally expensive and take a long time to process.In this paper, partial and selective encryption is proposed for medical images.First, deep learning based on U-Net is used to localize a tumor region called (ROI) a region of interest.A diffusion phase of the proposed system handles pixel values and positions based on linear and hyperchaotic systems.It includes converting an image's pixel values and repositioning pixels in a predetermined order.In the confusion phase, one level of Integer Discrete Wavelet Transform (IWT) is applied to divide the scrambled region into four sub-bands.Then, a Feistel network based on polynomial-based secret image sharing (SIS) encrypts the lowest frequency band only while the three bands LH, HL, and HH are diffused using a mapping technique based on the Morton scan to swap coefficients positions and then confused based on the hyperchaotic system.The culmination of these techniques results in generating a test image cipher characterized by robust confusion and diffusion properties.Importantly, this methodology has yielded remarkable results, reducing the encryption time by up to 96%.This efficiency is achieved without compromising the security or quality of the encrypted medical images.as high entropy is attained postencryption .Furthermore, by employing the Integer Discrete Wavelet Transform (IWT), the integrity and fidelity of the encrypted images remain uncompromised.Additionally, to bolster the level of confusion in the encryption process, a substantial key space of 2 1628 has been employed, further enhancing the resilience of the encryption method.
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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 | 0.000 |
| 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) | 0.001 | 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".