Partial Encryption Scheme of Medical Images Based on DWT, Secret Image Sharing and Hyperchaotic System
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
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.
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.
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 | 0.002 | 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) | 0.000 | 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