Improving the Security of the Medical Images
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
Applying security to the transmitted medical images is important to protect the privacy of patients. Secure transmission requires cryptography, and watermarking to achieve confidentiality, and data integrity. Improving cryptography part needs to use an encryption algorithm that stands for a long time against different attacks. The proposed method is based on number theory and uses Chinese remainder theorem as a backbone. This approach achieves high level of security and stands against different attacks for a long time. On watermarking part, the medical image is divided into two regions: a region of interest (ROI) and a region of background (ROB). The pixel values of the ROI contain the important information so this region must not experience any change. The proposed watermarking technique is based on dividing the medical image in to blocks and inserting the watermark to the ROI by shifting the blocks. Then, an equivalent number of blocks in the ROB are removed. This approach can be considered as lossless since it does not affect on the ROI, also it does not increase the image size. In addition, it can stand against some watermarking attacks such cropping, and noise.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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