Medical Image Encryption Based on Frequency Domain and Chaotic Map
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
Medical images typically have diagnostic and confidential data and information about the patient and are usually sent using public networks. Due to the sensitivity of medical images, their security has become a challenging requirement that must be addressed. Traditional cryptographical algorithms are inadequate to ensure appropriate and fine security while encrypting them, because of the correlation between each pixel, high redundancy of the image and its major size. Chaotic systems with their properties and partial encryption based on frequency domain are the best candidates for securing the storage and transfer of digital images. The paper shows new criteria for encryption of medical image. It is designed to improve performance and meet the increasing need for better security for medical image encryption. At first, the pixel's correlation is eliminated by scrambling the input image and diffusing them using secret sharing based on polynomials. Various frequency domains of the image are accomplished by applying the integer wavelet transform of the scrambled image, namely, the associated detail of (HL, LH and HH) and the LL (approximation coefficient) through using the AES algorithm. The (LL) part is encrypted to originate the diffusion image and by using the inverse of the Haar wavelet transform to produce a reliable, unbreakable and secure form. The designed algorithm is used to reverse and shuffle every frequency sign of the transformed image before transformation image back to the pixel domain. The original image form is restored through the reverse decryption algorithm. The suggested algorithm is measured and evaluated in a statistical way and normal standard security; The outcome of the proposed algorithm shows a strong resistant to the familiar attacks and extra secure than the other algorithms in the domain of image cryptography.
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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 | 0.001 | 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 | 0.000 |
| 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